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Contents

• Research and Evaluation Branch: 4
• This document: 4
• Disclaimer: 4
• Acknowledgements: 4
• Abbreviations: 5
• Glossary: 6

1. About the report: 8
2. Key conclusions: 8
3. Background and NDIS context: 10
4. What did we do?:
   • Overview of methods used: 12
5. What did we find?:
   • Summary of studies: 19
   • Characteristics of behavioural interventions: 19
   • Quality of the evidence used within this report: 25
   • Overall results across outcome measures: 26
   • Efficacy within individual outcome domains: 33
   • Investigating the effect of dose: 35
   • Investigating the effect of population, intervention, and study design factors on efficacy: 56
6. Limitations: 63
7. What did we learn?:
   • What benefits are likely?: 64
   • Is more intervention better?: 64
   • Who is best placed to deliver interventions?: 65
   • What other intervention design, implementation or participant factors impact outcomes?: 65
   • Considerations for practice: 65
   • Potential areas and considerations for follow-up work: 66
8. References: 68 Appendix A: Detailed study methodology:
   • Study objectives: 69

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• A2. Electronic search strategy - 70
• A3. Study selection and eligibility criteria - 72
• A4. Data collection and coding - 74
• A5. Assessing the quality of the evidence - 75
• A6. Data analysis - 75 Appendix B: Results - 79
  • B1. Study selection - 79
  • B2. Characteristics of included studies - 80
  • B3. Quality of the evidence used within this report - 92
  • B4. Efficacy across all and within individual outcome domains - 99
  • B5. Investigating the effect of dose - 127
  • B6. Investigating the effect of population, intervention, and study design factors on efficacy - 150 Appendix References - 160

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Research and Evaluation Branch

The Research and Evaluation Branch is responsible for ensuring that National Disability Insurance Agency (NDIA) policies, practices and priorities are informed by trustworthy and robust evidence so that decisions can be based on an understanding of what works, what doesn’t and the benefit to participants and the Agency.

This document

This report presents research findings from a systematic review and meta-analysis investigating the overall efficacy of behaviourally based interventions for children with autism spectrum disorder, as well as an investigation of contributing factors such as amount (dose) of intervention and other design (intervention type, person delivering, setting) and participant (age) factors.

Disclaimer

The NDIA accepts no responsibility for the accuracy or completeness of any material contained in this report. Further, the NDIA disclaims all liability to any person in respect of anything, and of the consequences of anything, done or omitted to be done by any such person in reliance, whether wholly or partly, upon any information presented in this report.

Material in this report is made available on the understanding that the NDIA is not providing professional advice. Before relying on any of the material in this report, users should obtain appropriate professional advice.

Views and recommendations of third parties, which may also be included in this report, do not necessarily reflect the views of the National Disability Insurance Agency, or indicate a commitment to a particular course of action.

Acknowledgements

The NDIA acknowledges the Traditional Owners and Custodians throughout Australia and their continuing connection to the many lands, seas and communities. The NDIA pays respect to Elders past and present, and extends this acknowledgement and respect to any Aboriginal and Torres Strait Islander people who may be reading this Report.

The Research and Evaluation Branch would like to acknowledge Dr Megan Clark (Olga Tennison Autism Research Centre, LaTrobe University), Professor Valsamma Eapen (UNSW Sydney), Professor Cheryl Dissanayake (Olga Tennison Autism Research Centre, LaTrobe University), Professor Andrew Whitehouse (Telethon Kids Institute), and Professor Adam Guastella (Brain and Mind Centre, Children’s Hospital Westmead Clinical School, University of Sydney) who assisted with the development of the project and provided expert advice throughout. We also acknowledge the broader NDIA team who assisted in the development of this research project.

© The National Disability Insurance Agency. Commented [GE1]: Currently investigating the use of the copyright symbol

You may distribute, remix, and build upon this work. However, you must attribute The National Disability Insurance Agency as the copyright holder of the work.

Suggested citation: National Disability Insurance Agency 2023. ‘Early behavioural interventions in children with Autism Spectrum Disorders: a systematic review and dose-response meta-analysis’. Prepared by E Ghezzi, A Lampit.

Enquiries about this publication should be addressed to: National Disability Insurance Agency. Email: redacted

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Abbreviations

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Abbreviation

Glossary

The behavioural features of autism are often present before a person is three years of age but in others they may not be recognised until their school years or later in life. The developmental challenges, signs and/or symptoms can vary widely in nature and degree between individuals, and in the same individual over time – that is why the term “spectrum” is used.

| Behaviourally based intervention | We define behaviourally based interventions as interventions for children with ASD which are underpinned by behavioural principles. These interventions span several intervention categories defined within the Autism CRC umbrella review (external) of non-pharmacological interventions for children with ASD. These categories include behavioural interventions (for example, applied behaviour analysis [ABA]), naturalistic developmental behavioural interventions (NDBIs), technology-based interventions, developmental interventions, TEACCH, and other (uncategorised) intervention types.

Behaviourally based interventions are typically delivered by trained clinicians but may also involve training parents or caregivers in behavioural principles to facilitate parent-delivered intervention. | | Clinician | Throughout the report, we use the term ‘clinician’ to refer qualified or trained individuals who deliver interventions. These are typically the providers of the intervention. |

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Dose was measured in two ways:

1. total clinician-delivered hours of the intervention,
2. monthly clinician-delivered hours of intervention (i.e., dose intensity).

| Meta-analysis | A meta-analysis uses statistics to combine the results from these studies to find out how much of an effect the intervention has on selected outcomes (which we call the effect size) and what factors can predict the size of the reported effects. |

| Parent | For clarity of writing, throughout this report we use the term ‘parent’ to refer to any individual who has parenting responsibilities for a child. |

| Systematic review | A systematic review summarises the evidence from research studies focused on the same topic. |

• People prefer different terms to describe autism. In our report, we used person first language (children with ASD) rather than identify first language (autistic child). We have used person first language to be consistent with how we refer to other target populations. We have used the term autism spectrum disorder (ASD) because that is the official diagnosis term and the language used in the research studies.

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1. About the report

This report summarises findings from a systematic review and meta-analysis of research studies investigating the benefits of behaviourally based interventions in children (less than 7 years old) with a diagnosis of autism spectrum disorder (ASD). A systematic review is a method for collecting evidence from studies of a particular topic. A meta-analysis involves synthesising this evidence statistically to arrive at quantifiable conclusions. Using this method, the benefits of behaviourally based interventions were investigated and how effects are related to the amount of intervention provided by clinicians (dose-response) was explored. An analysis of other intervention design factors and how these may relate to outcomes is also reported.

The project was conducted to assist the NDIA in developing evidence-based policy and practice guidance regarding the determination of reasonable and necessary supports for participants with autism under the age of 7. The Agency will consider this research evidence, alongside other important factors individual to each child and their circumstances, in determining what behaviourally based interventions are funded to help the child and family achieve their goals, aligned with the decision making criteria of the NDIS Act.

2. Key conclusions

Behaviourally based interventions are efficacious for key outcomes in children with ASD compared with children who undergo treatment as usual or non-behavioural interventions, but the pooled effect sizes are small (about 30% of a standard deviation) and vary considerably across studies.

Even for equivalent hours of clinician-delivered intervention, there is evidence for added benefit of behaviourally based intervention above that of treatment as usual (i.e., standard care or community-based intervention) or non-behaviourally based intervention.

With dose relationships varying based on the outcomes of the intervention, decisions regarding the amount of intervention provided should take into account the specific goals of the participant and the planned outcomes of the intervention.

• For goals related to the ASD characteristics (i.e., socialisation, social affect, challenging behaviours, restricted repetitive behaviours, etc) or cognition and language, benefit of behaviourally based interventions can be seen at low total doses and dose intensities. There seems to be very little added benefit of increased hours and intensity of intervention for cognition and language, and no evidence for added benefit with increased dose in the case of ASD characteristics, which means that many participants may benefit from less intervention hours.
• If the intervention is specifically intended to target adaptive functioning, there may be no benefit of behaviourally based interventions below approximately 800 total hours or an intensity of 65 hours per month. With such high dose requirements, intervention approaches other than behavioural based interventions may be more feasible and cost-effective, unless supported by evidence for the specific intervention requested.

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Importantly, just focusing on dose by itself is a weak predictor of outcomes of behaviourally based intervention in children with ASD and should only be considered as one factor in a treatment decision to ensure alignment to the holistic goals of the child.

No differences were found for benefit of behaviourally based interventions by type of intervention, type of control group, primary intervention setting, person delivering the intervention, or age of the participant. With no differences found in the intervention and design factors investigated, it is likely that all factors investigated could be useful in the right context. This emphasizes the importance of tailoring the intervention design to the unique context and goals of the child and their family.

With no evidence to suggest that interventions by a parent are inferior to those delivered by clinicians, this warrants further investigation of parent-delivered interventions. This report did not assess factors which may contribute to the success of these interventions, such as parent training (duration, content, etc), support for parents throughout the intervention (type, amount), and fidelity.

To further the work presented here, other meta-analysis methods would allow for examining differences in individual circumstances as well as other participant-level (e.g., autism severity) and intervention-related factors to be explored in more detail.

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3. Background and NDIS context

Autism Spectrum Disorder (ASD) is the collective term for a group of neurodevelopmental conditions which affect brain growth and development. Characteristics of ASD vary greatly in nature and degree, but may involve challenges with social interaction and communication, sensory issues, and restricted and repetitive behaviours, interests, or activities [1]. Behavioural features of ASD are often present before the age of 3 years old but may not be recognised until later in life.

ASD is the largest primary disability category in the NDIS, encompassing 34% of active participants. As of December 2022, 22,018 NDIS participants under the age of 6 had a primary disability of autism, representing approximately one-quarter of NDIS participants in this age group. Intervention during childhood represents an important opportunity to support early development and build on the child’s strengths. Through the NDIA’s early childhood approach, children under 7 years old can access NDIS funding for early intervention supports. The NDIA’s early childhood approach is based on the National Guidelines for Best Practice in Early Childhood Intervention (external), emphasising the central role of family as well as development in natural, everyday settings [2]. These principles are promoted within the Autism CRC National Guideline (external) [3] for supporting children with ASD and their families, where similar recommendations are made for approaches which are child and family-centred, individualised, and strengths-focused [3].

A wide range of non-pharmacological interventions are available for children with ASD, all of which aim to assist early development and skill acquisition across domains (e.g., social affect, cognition, adaptive functioning). Behavioural principles underpin a considerable range of these interventions (see Appendix 1 for list of interventions), which span several intervention categories defined within the Autism CRC umbrella review (external) of non-pharmacological interventions for children with ASD [4]. These categories include behavioural interventions (for example, applied behaviour analysis [ABA]), naturalistic developmental behavioural interventions (NDBIs), technology-based interventions, developmental interventions, TEACCH, and other (uncategorised) intervention types.

Behaviourally based interventions are typically delivered by trained clinicians but may also involve training parents or caregivers in behavioural principles to facilitate parent-delivered intervention. It is estimated that at least 7,936 participants under the age of 7 with a primary disability of ASD received some form of capacity building support (which may include behaviourally based interventions) funded through the NDIS early childhood services in 2021.

There is currently low to moderate evidence supporting the efficacy of behavioural interventions for core autism characteristics which includes communication, cognition, behaviour, school readiness and academic skills [4]. While some evidence exists, there is limited and varied evidence which reports the effect of amount (“dose”) of behaviourally based interventions on outcomes. Additionally, results of dose response investigations vary by their focus and research methods. For example, one systematic review reported benefits of higher intensity (hours per week) of intervention for cognition and adaptive behaviour, but not language, and found no effect of total intervention duration [5]. Another meta-analysis identified a potential linear association between hours of intervention and benefit to adaptive functioning and language outcomes [6]. Finally, a meta-analysis of individual

participant data reported larger effect sizes for overall ASD characteristics after 24 months of

intervention compared to 12 months [7].

With limited understanding of the optimal dose of behaviourally based interventions for children with ASD, there are currently no evidence-based guides or best practice principles available, which has led to inconsistency in service provision and participants receiving varied hours of an intervention. Confusion associated with this can be distressing for parents, as it is unclear how many hours are necessary for their child to achieve the best outcomes. With such variable results and a general lack of investigation of contributing factors (i.e., setting, intervention characteristics, participant characteristics, etc) [8], it is difficult to use existing evidence to guide NDIA policy and operations. Crucially, evidence regarding dose of interventions is only one variable which can inform an individualised decision about what is needed for a particular child within their unique environmental context and family circumstances. As such, it is important to have a body of evidence of what works, at what dose, for who, in what context and to what end, to reduce confusion, strengthen guidance, and ensure the best outcomes for participants are achieved. One of the national best practice principles for childhood intervention is that interventions and practice must be research-based, so creating a body of evidence to support NDIA policy and practice is an essential part of how the NDIA must discharge its decision making responsibilities regarding funding for reasonable and necessary supports under the NDIS Act.

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4. What did we do?

A comprehensive systematic review and meta-analysis was undertaken to identify the overall efficacy of behaviourally based interventions for various outcomes in children with ASD and, importantly, how contributing factors impact these outcomes.

The objectives of the systematic review and meta-analysis were to:

• examine the evidence for the efficacy of behaviourally based interventions in children under 7 years with ASD on child (functional and developmental) and family outcomes; and
• investigate how effects are related to dose (amount) of intervention as well as other factors relating to study design, intervention, comparison group, and child characteristics.

4.1 Overview of methods used

Findings included in this report were identified through a systematic review and meta-analysis. A systematic review is a process to locate and summarise the results of all studies that ask a particular research question, usually by using different methods with a common underlying question (e.g., are behaviourally based interventions efficacious in improving adaptive functioning in children with ASD?). A meta-analysis is a statistical procedure that combines results from the studies identified in a systematic review to find a common estimate of effect between studies, as well as how effects might vary across settings and other factors (e.g., age, intervention type).

A full description of the study methods is available in Appendix 1.

4.1.1 Search and screening of articles

Five databases were searched to identify all published studies that examined the impact of a behavioural intervention on a range of outcomes in children under the age of 7-years with autism spectrum disorder. The search screening process is in Appendix 1 and criterion for inclusion are outlined below by Population, Intervention, Comparison, and Outcome (i.e., PICO).

Population

Studies were eligible if they included children who:

• are 7-years old or younger at the beginning of the intervention,
• AND have a diagnosis of autism spectrum disorder (or have a high likelihood of ASD for children less than 3 years)

Intervention

Studies were eligible if they included behaviourally based interventions (typical interventions listed in Appendix 1).

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Comparison

Studies were eligible if they included a comparison group which comprised of children 7-years old or younger with autism spectrum disorder who:

• continued standard care or treatment as usual (i.e., community interventions),
• were on a waitlist,
• OR completed an alternate, non-behaviourally based intervention.

Studies without a comparison group (i.e., single arm studies) and case studies were excluded.

Outcomes

Studies were eligible if they reported outcomes that were measured both:

• before intervention has begun,
• AND following intervention.

The following five domains were eligible for inclusion:

1. ASD characteristics a. Global measures of ASD characteristics and behaviours b. Emotional regulation c. Restricted repetitive behaviours/sensory d. Social affect (foundational social skills) e. Socialisation (application and competence in using social skills) f. Challenging behaviours
2. Cognitive and language outcomes a. Cognition (verbal and nonverbal cognitive abilities and motor skills) b. Language (receptive and expressive language and verbal communication)

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Interventions

Interventions may be delivered to children:

• face-to-face,
• OR via telehealth.

Interventions may be delivered to children by:

• qualified or trained individuals,
• parents,
• caregivers,
• teachers,
• OR a combination of these.

Interventions may be:

• one-to-one,
• OR in a small group format.

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3. Functional outcomes

• Adaptive behaviour (everyday functioning e.g., Vineland Adaptive Behaviour Scales)
• Education outcomes (e.g., education setting/level of support)

4. Family outcomes

• Caregiver or family wellbeing
• Quality of life (child, caregiver, overall family unit)

5. Adverse effects

• Child distress (e.g., anxiety/depression)
• Parent stress/burden (e.g., Parenting Stress Index)
• Reduced participation in mainstream settings (e.g., reduced participation in preschool)

For inclusion, each study must report at least one of these outcomes.

4.1.2 Combining effects from included studies

Intervention effects within each outcome domain (see Section 4.1.1 for domain descriptions) were combined across studies using meta-analysis. The intervention effect was measured using standardised mean difference, calculated as Hedges’ g, with 95% confidence interval (CI).

Hedges’ g provides the difference (effect) between two groups in standard deviation units. This allows us to combine the intervention effects from the different outcome domains into a single analysis. A positive Hedges’ g means that the intervention was beneficial over the comparison group.

The confidence interval estimated the precision of the estimate of effect. When the confidence interval includes the null (i.e., when the lower bound of the interval is below zero), the effect estimate is too imprecise to be considered statistically significant, meaning there is not enough information to determine whether the intervention is beneficial or not.

Each pooled estimate is provided along with a measure of statistical heterogeneity, denoted as tau- squared (Tau2). This gives an estimation of the extent to which an effect estimate is inconsistent across studies.

4.1.3 Investigating a range of contributing factors to efficacy

To investigate variability in these combined efficacy estimates (i.e., heterogeneity), further analyses were conducted to explore the effect of dose as well as other study, intervention, and population- based factors. The factors examined within subgroup analyses were (1) person delivering intervention; (2) intervention type; (3) comparison group type; (4) age group; (5) primary intervention setting; and (6) study design.

These factors are critical as they are overarching characteristics of the study, intervention and participants which may impact the efficacy of the intervention. It is also useful to know what types of interventions work best, what settings are associated with best outcomes, whether there are better

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Behavioural Interventions for Children with ASD

This page (page 15 of 667) details intervention categories and comparison groups.

Person delivering intervention

• Clinician (i.e., clinician, facilitator, or provider)
• Clinician and parent
• Parent (i.e., parent or caregiver)
• Teacher (i.e., early educator or teacher)

Intervention category

The six intervention categories are based on Autism CRC definitions [4].

• Behavioural, for example:
  • Early Intensive Behavioural Intervention (EIBI)
  • Applied Behavioural Analysis (ABA)
  • Discrete Trial Teaching (DTT)
  • Picture exchange communication system (PECS)
• Naturalistic Developmental Behavioural Interventions (NDBIs), for example:
  • Early Start Denver Model (ESDM)
  • Pivotal response treatment (PRT)
  • Joint Attention, Symbolic Play, Engagement, and Regulation (JASPER)
• Developmental, for example:
  • DIR Floortime
• Technology-based, for example:
  • GOLIAH
• TEACCH (a discrete intervention)
• Other interventions, for example:
  • Autism 1-2-3
  • Learning Experiences and Alternative Program for Preschoolers and their Parents (LEAP)
  • Parent-Child Interaction Therapy (PCIT)

Comparison group

• Treatment as usual (TAU), for example:
  • Waitlist controls
  • Usual or routine care, often in the community (e.g., speech and language therapy).
  • Regular or non-specific specialised school-based services.
  • Public education or psychoeducation for parents.

Investigating the Effect of Dose

To ensure results reflect current practice in the Australian context, dose was defined as clinician- delivered hours of intervention as this is the main delivery method funded through the NDIS. Specific parent-delivered interventions and interventions in early education settings (often delivered by teachers) were excluded from the dose analyses. It is important to note these studies were still included in the main efficacy analyses, as well as subgroup analyses investigating the effects of study, intervention and population characteristics.

Dose was measured in two ways:

• total clinician-delivered hours of the intervention,
• monthly clinician-delivered hours of intervention (i.e., dose intensity).

It is worth noting that duration (weeks) of intervention was not accounted for or further explored within this report. Nevertheless, duration of intervention had a large association with both total hours of intervention (r = 0.8, p < 0.001) and monthly hours of intervention (r = 0.8, p < 0.001), and herefore its potential effect on the results is limited.

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The effect of the dose of interventions was explored using three methods, listed here and further detailed below:

1. Relationship between dose and efficacy
2. Comparing efficacy for lower versus higher total and monthly dose
3. Relationship between dose and change from baseline to follow-up separately within the: a. Behaviourally based intervention group b. Comparison group

1. Relationship between dose and efficacy

Linear and non-linear models were used to explore the relationship between dose (total and monthly clinician-delivered hours) and efficacy (Hedges’ g) of the intervention (see Appendix 1 for description) across each of the outcome domains (where data permits).

As described previously, efficacy (Hedges’ g) of the behaviourally based intervention (as compared to the comparative group) was calculated for each outcome domain reported in each study. For each outcome domain, the efficacy of each individual study was then plotted against the dose of intervention implemented within that study to visualise the dose relationship.

These relationships were then significance tested to assess the likelihood of a relationship between dose and efficacy. The model estimate (β) indicates the size of the relationship, with a positive model estimate indicating a positive relationship (as dose increases, so does efficacy) and a negative model estimate indicating a negative relationship (as dose increases, efficacy decreases). The model estimate (β) can be interpretated as the added benefit (in Hedges’ g) associated with each additional hour of intervention. For example, if β=0.01, the effect size is estimated to increase by g=0.1 (i.e., 10% standard deviation difference) for every 10 additional hours.

2. Comparing efficacy for lower versus higher total and monthly dose

The dose analyses were corroborated by investigating differences in the efficacy of lower versus higher total dose as well as lower versus higher dose intensity (monthly hours), with lower and higher defined in both cases based on a median split. Lower and higher intervention doses were also directly compared within each outcome domain.

3. Relationship between dose and change from baseline to follow-up separately within the intervention group and the comparison group

This analysis involved the calculation of an effect size (Hedges’ g) separately for the behaviourally based intervention group and the comparison group in each study. The resulting effect size is the change between two time-points: baseline (pre-intervention) and follow-up. A positive Hedges’ g means that the group improved from baseline to follow-up on that outcome domain.

The effect size for the change in the behaviourally based intervention group represents the overall effect of the intervention, which includes the specific effect of intervention components as well as non-specific factors such as repeated measures and expectations (‘placebo effect’). Conversely, the effect size for the change in the comparison group represents only the non-specific factors, such as those associated with treatment as usual in the community. Thus, if the effect size within the

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The intervention group is larger than that of the comparison group, the intervention offers a benefit beyond what would be expected from treatment as usual.

As was described for the dose response analyses (1. Relationship between dose and efficacy, above), linear and non-linear models were then used to explore the relationship between dose (total and monthly clinician-delivered hours) and change from baseline to follow-up (Hedges’ g). These relationships were explored for each outcome domain (as outlined in Section 4.1.1), for both the intervention and comparison groups separately. Dose in the comparison groups was again recorded as clinician-delivered hours of intervention, and may include services such as occupational therapy, speech therapy, etc. This analysis allows an assessment of the difference in effect of behaviourally based intervention and treatment as usual, both with the same clinician-delivered hours.

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5. What did we find?

The following section reports the key findings from the analysis. A more detailed description of results is available in Appendix 2.

5.1 Summary of studies

Overall, 98 studies were included, representing a total of 4,553 participants. These studies were conducted across 21 countries, predominantly the US (45 studies), followed by the UK (7 studies), Norway (6 studies), Australia (4 studies), Canada (4 studies), and Italy (4 studies), among others. Half of included studies were randomised controlled trials (50%), with the remainder non-randomly allocating participants to intervention or comparison groups (e.g., by caregiver preference or using existing groups), or using existing cohorts of participants. The age of study participants ranged from 9 months to 7.1 years with an overall mean of 3.8 years, and 84% of study participants were male. Further characteristics of the included studies are shown in Appendix 2.

Across the 98 studies, 1,560 outcome measures which met the criteria outlined in Section 4.1.1 were reported, with an average of 16 outcome measures reported per study. The number of studies that reported outcomes within each domain varied: 81 studies reported ASD characteristic outcomes; 47 reported adaptive functioning outcomes; 64 reported cognition and language outcomes; 20 reported family outcomes; and 27 reported adverse effect outcomes.

5.2 Characteristics of behavioural interventions

Interventions vary on several characteristics (subgroups) which include content, person delivering the intervention, and primary setting (see Section 4.1.3). The differences these characteristics have on outcomes following a behaviourally based intervention is explored in the following sections. To summarise the distribution of identified studies across these characteristics, Table 1 shows the number of studies which fall under each category by outcome domain as well as the average dose of intervention across subgroup categories.

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Table 1a. Number of studies and dose amount by person delivering intervention.

Notes: Number of studies is reported in total and across all five outcomes domains by subgroup level. Total and monthly clinician-delivered are across all studies which report that data within each subgroup level. NA = not applicable; ASD = ASD characteristics; AF = Adaptive functioning; CL = Cognition and language; FO = Family outcomes; AE = Adverse effects.

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Table 1b. Number of studies and dose amount by intervention category.

Notes: Number of studies is reported in total and across all five outcomes domains by subgroup level. Total and monthly clinician-delivered are across all studies which report that data within each subgroup level. NA = not applicable.

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Table 1c. Number of studies and dose amount by comparison group.

Notes: Number of studies is reported in total and across all five outcomes domains by subgroup level. Total and monthly clinician-delivered are across all studies which report that data within each subgroup level. NA = not applicable.

Table 1d. Number of studies and dose amount by age group.

Notes: Number of studies is reported in total and across all five outcomes domains by subgroup level. Total and monthly clinician-delivered are across all studies which report that data within each subgroup level. NA = not applicable.

Table 1e. Number of studies and dose amount by primary intervention setting.

Notes: Number of studies is reported in total and across all five outcomes domains by subgroup level. Total and monthly clinician-delivered are across all studies which report that data within each subgroup level. NA = not applicable.

Table 1f. Number of studies and dose amount by study design.

Notes: Number of studies is reported in total and across all five outcomes domains by subgroup level. Total and monthly clinician-delivered are across all studies which report that data within each subgroup level. NA = not applicable.

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5.2.1 Clinician-delivered dose of intervention

A total of 34 studies reported clinician-delivered dose. Due to limited evidence for family outcomes and adverse effects, dose-response analyses were not included for these outcome domains.

Total clinician-delivered dose (Figure 1) varied greatly across studies, ranging from 7 to 5088 hours (median = 137 hours). Monthly clinician-delivered hours (Figure 2) also had a wide range, from 2 to 158 hours per month (median = 27 hours).

Both total (r = 0.8, p < 0.001) and monthly (r = 0.8, p < 0.001) clinician-delivered hours were significantly associated with total duration (weeks) of the intervention, meaning that longer intervention durations typically meant more total and monthly clinician-delivered hours of intervention. Because of this association, the unique effect of duration is unlikely to be substantial and duration was not explored further within the analyses relating to dose.

The clinician-delivered dose of an intervention varied across subgroups. Average clinician-delivered dose and range by subgroup is shown in Table 1.

Figure 1. Total clinician-delivered hours of intervention within included studies

     30
           25
     25

     20

     15     studies
 N 10
                      6
      5
                               1         1                   1                                                   0
      0
            [10, 1010]  (1010, 2010] (2010, 3010] (3010, 4010] (4010, 5010] (5010, 6010]

                                  Total clinician‐delivered hours

Figure 2. Monthly clinician-delivered hours of intervention within included studies

    25
           22
    20

    15
   studies 10
N                   6
     5                          4
                                           2
     0
               [2, 51]      (51, 100]   (100, 149]   (149, 198]

              Monthly clinician-delivered hours

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5.3 Quality of the evidence used within this report

5.3.1 Randomised controlled trials

Risk of bias was evaluated for the 49 randomised controlled trials included in this report. Of the overall assessments, 28 were deemed to have a high risk of bias, 20 had some concerns, and one was deemed low risk. Assessments of risk of bias within individual bias domains are summarised in Figure 3 (see Table B2 for individual domain assessments by study). The high proportion of unclear risk in the selection of the reported result was due to few studies providing a pre-specified analysis plan. A considerable number of studies (>25%) had high risk of bias in the measurement of outcomes due to the lack of blinding for outcome assessors. Approximately 25% of studies did not specify the use of a randomisation process which was concealed prior to enrollment and assignment to intervention, resulting in a high risk of bias for randomisation process. Relatively low risk of bias due to deviations from intended interventions and missing outcome data were identified.

Figure 3. Risk of bias across outcome domains in randomised controlled trials.

Note: Purple indicates high risk assessment. Green indicates low risk assessment. Pink indicates risk assessment of some concerns.


5.3.2 Non-randomised study designs

Risk of bias was assessed separately for the 49 studies which employed non-randomised study designs. Overall assessments deemed 38 to have serious risk of bias, and 11 to have a moderate risk of bias. Assessments of risk of bias within individual bias domains are summarised in Figure 4 (see Table B3 for individual domain assessments by study). Moderate or serious risk of bias due to confounding were seen across studies because of a lack of measurement or control of important cofounders (e.g., age, autism severity, IQ) within analyses. Moderate or serious risk of bias due to missing data were seen in >25% of studies due to the presence of substantial missing data (>10%) which was often unequal between groups. Greater than 50% of studies showed evidence of bias in the measurement of outcomes because assessors were unblinded. Predominantly moderate risk of bias in the selection of the reported result was shown due to the general absence of pre-specified analysis plans.

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Figure 4. Risk of bias across outcome domains in non-randomised study designs.

Note: Purple indicates serious risk assessment. Green indicates low risk assessment. Pink indicates moderate risk assessment.

[Image not converted to Markdown – “Risk of bias across outcome domains in non-randomised study designs” – check the source PDF page for the actual content]

Commented [GE3]: Alt text: Displays percent serious, moderate and low risk assessments across seven domains. For ‘bias due to confounding’: 35% serious risk, 65% low risk. For ‘bias in selection of participants’: 6% serious risk, 4% moderate risk, 90% low risk. For ‘bias in classification of interventions’: 2% serious risk, 2% moderate risk, 96% low risk. For ‘bias due to deviations from intended interventions’: 2% moderate risk, 98% low risk. For ‘bias due to missing data’: 25% serious risk, 10% moderate risk, 65% low risk. For ‘bias in measurement of outcomes’: 59% serious risk, 4% moderate risk, 37% low risk. For ‘bias in selection of the reported result’: 6% serious risk, 94% moderate risk.

5.4 Overall results across outcome measures

Overall results across measures: a summary of findings

When considering the combined evidence across all outcome measures:

• There was a small benefit of behaviourally based interventions when compared to treatment as usual, waitlist, or non-behaviourally based, “eclectic” interventions (the comparison groups). This small benefit was found, regardless of the dose intensity (monthly clinician-delivered hours) of the intervention.
• A dose relationship was found, with increased total clinician-delivered hours associated with better outcomes following behaviourally based interventions than the comparative group.
  • However, the associated real-world impact is arguably inconsequential:
    • Every additional 100 hours of behaviourally based intervention a child receives translates to less than a 1% increase in standard deviation for the effect estimate.

5.4.1 Efficacy

Overall, there is evidence to support behaviourally based intervention in children less than 7 years old with ASD. Benefit of behaviourally based interventions when compared with treatment as usual, waitlist or a non-behaviourally based, “eclectic” intervention was shown across reported outcomes within the 98 studies but with a small effect size (Hedges’ g = 0.32, 95%CI 0.26 – 0.38, Prediction Interval = -0.33 – 0.98, tau2 = 0.11).

There was some evidence that effect sizes were larger in smaller studies, which can be indicative of over-estimation of treatment effect for this outcome.

after estimating the bias, the effect estimate

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was reduced from 0.32 (95%CI 0.24 to 0.39) to 0.23 (95%CI 0.17 to 0.29). This estimated effect size which accounts for this bias is reported in Appendix 2.

5.4.2 Relationship between dose and efficacy

Across the 34 studies the linear models show a statistically significant relationship between increasing total clinician-delivered hours of a behaviourally based intervention and better outcomes (see Figure 5). However, while the linear dose response trend for increasing total hours of intervention was statistically significant, the associated real-world impact is arguably inconsequential, because every increase of 100 total hours of intervention translates to less than a 1% increase in standard deviation for the effect estimate.

There was no significant relationship between monthly clinician-delivered hours of behaviourally based intervention and effect estimate (Model estimates can be found in Table B10). The non-linear modelling demonstrated similar results (see Figure 5), with only slightly improved model fit.

Figure 5a. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for all outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Image of graph displaying linear relationship between monthly clinician-delivered hours and Hedges’ g” – check the source PDF page for the actual content]

Commented [GE4]: Alt text: Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for all outcomes. There is a slight upward trend, with Hedges’ g beginning at approximately 0.2 and ending at approximately 0.4. The 95% confidence interval widens with increasing monthly clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Figure 5b. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for all outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.


Commented [GE5]: Alt text: Displays the linear relationship between total clinician-delivered hours (from 0 to 5000 hours) and Hedges’ g for all outcomes. There is a slight upward trend, with Hedges’ g beginning at approximately 0.2 and ending at approximately 0.5. The 95% confidence interval widens with increasing total clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Much fewer data points are available for higher clinician-delivered hours. Values mentioned here are approximate.

Figure 5c. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for all outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.


Commented [GE6]: Alt text: Displays the non-linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g for all outcomes. Hedges’ g values remain relatively stable from 0 to 75 hours, after which there is a gradual increase in Hedges’ g from approximately 0.2 to approximately 0.6. The 95% confidence interval is relatively wide (about 0.4 Hedges’ g). Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Figure 5d. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for all outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Figure 5d” – check the source PDF page for the actual content]

Displays the non-linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for all outcomes. There is a slight upward trend, with Hedges’ g beginning at approximately 0.2 and ending at approximately 0.5. The 95% confidence interval widens with increasing total clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Much fewer data points are available for higher clinician-delivered hours. Values mentioned here are approximate.

5.4.3 Comparing efficacy for lower versus higher total and monthly dose

The relationship between dose (both total and monthly hours of intervention) and efficacy was compared with lower and higher clinician-delivered hours (based on a median split of the data). No difference in efficacy was shown between lower and higher total and monthly doses of intervention, with small benefit shown at both dose levels (see Figure 6 and Table 2).

Table 2. Results of analyses comparing lower and higher total and monthly doses for all

outcome measures.

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Figure 6a. Linear dose relationship for lower versus higher monthly clinician hours (based on median) for all outcome measures.

Note: The green shaded area indicates less than the median number of monthly clinician-delivered hours, and the pink shaded area indicates higher than the median number of monthly clinician- delivered hours for this outcome.


Commented [GE8]: Alt text: Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g. Coloured shading is used to differentiate between hours below and above the median number of hours. There is a slight upsward trend, with Hedges’ g beginning at approximately 0.2 and ending at approximately 0.4. The 95% confidence interval widens with increasing monthly clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

Figure 6b. Linear dose relationship for lower versus higher total clinician hours (based on

median) for all outcome measures.

Note: The green shaded area indicates less than the median number of total clinician-delivered hours, and the pink shaded area indicates higher than the total number of monthly clinician- delivered hours for this outcome.


Commented [GE9]: Alt text: Displays the linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g. Coloured shading is used to differentiate between hours below and above the median number of hours. There is a slight upsward trend, with Hedges’ g beginning at approximately 0.2 and ending at approximately 0.5. The 95% confidence interval widens with increasing total clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Much fewer data points are available for higher clinician- delivered hours. Values mentioned here are approximate.

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5.4.4 Relationship between dose and change from baseline to follow-up separately within the intervention group and the comparison group

When investigating change in outcomes over time from baseline to follow-up within the intervention group (regardless of the comparison group), linear models show a statistically significant relationship, indicating that increasing total and monthly clinician-delivered hours of an intervention translates to improved outcomes (see Figure 7). Model results and graphs of individual models with 95% confidence intervals can be found in Table B11 and Figure B14. This means that, without controlling for the comparison group, increasing dose translates to better outcomes in the intervention group.

This relationship between dose (total and monthly) and change in outcomes over time from baseline to follow-up was not replicated in the comparison group (see Table B12 for model results). Non-linear models can be seen in Figure 7, with models with 95% confidence intervals shown in Figure B18. This demonstrates that increasing clinician hours of treatment as usual intervention (most comparison groups involved treatment as usual or standard care in the community) does not translate to better outcomes.

Figure 7a. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in all outcomes from pre- to post-intervention within intervention and comparison groups.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

Commented [GE10]: Alt text: Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group remains constant at 0.25 across hours. Hedges’ g for the intervention group increases from 0.25 at 0 hours to 1.0 at 150 hours. Values mentioned here are approximate.

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Figure 7b. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for change

in all outcomes from pre- to post-intervention within intervention and comparison groups.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Linear model of total clinician-delivered dose by effect size” – check the source PDF page for the actual content]

Displays the linear relationship between total clinician-delivered hours (from 0 to 3000 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group increases slightly from 0.2 at 0 hours to 0.4 at 2000 hours. Hedges’ g value for the intervention group increases from 0.45 at 0 hours to 0.6 at 3000 hours. Values mentioned here are approximate.

Figure 7c. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for

change in all outcomes from pre- to post-intervention within intervention and comparison groups.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Non-linear model of monthly clinician-delivered dose by effect size” – check the source PDF page for the actual content]

Displays the non-linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group increases from 0 at 15 hours to 0.4 at 50 hours, then decreases to 0.2 at 100 hours, where it remains relatively stable. Hedges’ g value for the intervention group increases from 0.2 at 0 hours to 0.75 at 75 hours, then decreases to 0.5 at 150 hours. Values mentioned here are approximate.

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Figure 7d. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in all outcomes from pre- to post-intervention within intervention and comparison groups.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Figure 7d” – check the source PDF page for the actual content]

Alt text: Displays the non-linear relationship between total clinician- delivered hours (from 0 to 3000 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group increases from 0.1 at 0 hours to 0.3 at 400 hours, then decreases to 0 at 100 hours, then increases to 0.5 at 2000 hours. Hedges’ g value for the intervention group remains stable at 0.5 for all hours. Values mentioned here are approximate.

5.5 Efficacy within individual outcome domains

Efficacy within individual outcome domains: summary of findings

When the efficacy of the five outcome domains were investigated separately, evidence showed:

• There was a small benefit of behaviourally based interventions when compared to treatment as usual, waitlist, or non-behaviourally based, “eclectic” interventions on measures of ASD characteristics, adaptive functioning, cognition and language, family outcomes and reductions in adverse effects (i.e., parent stress).
• There were large variances in effect estimates between studies (i.e., heterogeneity). This heterogeneity may be related to differences among studies, such as population, intervention, and study design factors. Analyses of some of these factors are provided in Sections 6.6 and 6.7 below.

The efficacy of behaviourally based interventions was investigated across five outcome domains, which represent typical autism characteristics and behaviours, including ASD characteristics, adaptive functioning, and cognition and language, as well as family outcomes and reductions in adverse effects (i.e., child and parent stress). These outcome domains are detailed in Section 4.1.1.

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Small effect sizes for outcomes in all five outcome domains were identified (see Figure 8 for individual estimates). However, they represent better performance in the behaviourally based intervention group when compared with the comparative group across all outcomes.

There was some evidence that effect sizes for the ASD characteristics outcome domain were larger in smaller studies, which can be indicative of over-estimation of treatment effect for this outcome. After estimating the bias, the effect estimate was reduced from 0.32 (95%CI 0.24 to 0.39) to 0.22 (95%CI 0.15 to 0.29). This estimated effect size which accounts for this bias is reported in Appendix 2.

While pooled effect estimates for all five domains are statistically significant (confidence intervals do not include 0), there is large variability in the prediction intervals (i.e., the range of true effects across studies) (see Figure 8). This large variability indicates that some behaviourally based interventions may have no effect on investigated outcomes.

Figure 8. Forest plot of pooled effect sizes of overall efficacy for specific domains

Note: An accessible version of the data displayed in this figure is presented in Table 3 below. The prediction interval indicates the range of true effects across studies. Tau² is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

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Table 3. Table version of forest plot of pooled effect sizes of overall efficacy for specific domains

Note: This table presents the information displayed in Figure 8 in an accessible format. The prediction interval indicates the range of true effects across studies. Tau2 is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

It is important to explore this variability to find what factors are associated with best outcomes and what should be avoided. This was achieved by investigating changes in the effect across different factors (subgroups) and differences between these. In the following sections, the impact of dose, intervention (content, delivery, setting) and population (age) subgroups were investigated. These subgroups are detailed in Section 4.1.3.

5.6 Investigating the effect of dose

Investigating the effect of dose: summary of findings

ASD characteristics

• No dose relationship was found between total or monthly clinician-delivered intervention hours and ASD characteristic outcomes. o This means that there is no evidence for added benefit of increasing intervention hours or intensity for ASD characteristic outcomes. Small benefits of behaviourally based interventions above that of the comparison group are consistently seen, regardless of the total or monthly clinician-delivered hours received.

Adaptive functioning

• A dose relationship was found, with increased total and monthly clinician-delivered hours associated with better adaptive functioning outcomes following behaviourally based interventions. o The associated real-world impact of relatively smaller increases in intervention hours is minimal.  Every increase in the total intervention dose by 100 hours translates to less than a 1% increase in standard deviation for the effect estimate.

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• Every increase in the intervention dose intensity by 10 hours per month translates to an increase of only 3% of a standard deviation of the effect estimate.
  • Doses below approximately 65 hours per month or 800 total intervention hours may not produce the desired effect (inefficacious).
• Increasing efficacy with increased total and monthly hours may be driven by decreasing outcomes with more clinician-delivered intervention hours in comparison group, rather than a specific benefit of behaviourally based interventions.

Cognition and language

• A dose relationship was found, with increased total and monthly clinician-delivered hours associated with better cognition and language outcomes following behaviourally based interventions.
  • No evidence for a minimal required dose. Small, positive intervention effects are present even at lower total dose amounts and dose intensities, but slightly increasing with additional hours.
  • The associated real-world impact of relatively smaller increases in intervention hours is minimal.
    • Every increase in the total intervention dose by 100 hours translates to less than a 1% increase in standard deviation for the effect estimate.
    • Every increase in the intervention dose intensity by 10 hours per month translates to an increase of only 2% of a standard deviation of the effect estimate.
• A relationship between increasing total clinician hours and improved outcomes was found for both the intervention and comparison groups, suggesting that the finding of better outcomes with increased hours is likely related to the amount of time spent with a clinician than the actual intervention taking place.

5.6.1 ASD characteristics

Relationship between dose and efficacy

The linear and non-linear models indicate no dose relationship between total or monthly clinician- delivered hours and efficacy of behaviourally based interventions for ASD characteristics (see Figure 9). This shows that increasing the dose of an intervention (total or monthly clinician hours) does not change the efficacy of behaviourally based interventions for ASD characteristics.

Linear model estimates are reported in Table B10.

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Figure 9a. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for ASD characteristics outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for ASD characteristics outcomes.” – check the source PDF page for the actual content]

Alt text: Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for ASD characteristic outcomes. There is no change in Hedges’ g with increasing hours, with Hedges’ g remaining stable at approximately 0.25.,The 95% confidence interval widens with increasing monthly clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

Figure 9b. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for ASD characteristics outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Linear model of total clinician-delivered dose by effect size (Hedges’ g) for ASD characteristics outcomes.” – check the source PDF page for the actual content]

Alt text: Displays the linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for ASD characteristic outcomes. There is no change in Hedges’ g with increasing hours, with Hedges’ g remaining stable at approximately 0.25. The 95% confidence interval widens with increasing total clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Much fewer data points are available for higher clinician-delivered hours. No data is available beyond 4000 hours. Values mentioned here are approximate.

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Figure 9c. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for ASD characteristics outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.


Displays the non-linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g for ASD characteristic outcomes. Hedges’ g values increase from 0 to approximately 20 hours, from Hedges’ g of 0 to 0.5. Hedges’ g then decreases to approximately 0.1 by 60 hours. From there, there is a gradual increase with increasing hours up to approximately 0.5. The 95% confidence interval is relatively wide (about 0.4 Hedges’ g). Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

Figure 9d. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for ASD characteristics outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.


Displays the non-linear relationship between total clinician-delivered hours (from 0 to 5000 hours) and Hedges’ g for ASD characteristic outcomes. There is no change in Hedges’ g with increasing hours, with Hedges’ g remaining stable at approximately 0.25. The 95% confidence interval widens with increasing total clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Much fewer data points are available for higher clinician-delivered hours. No data is available beyond 4000 hours. Values mentioned here are approximate.

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Comparing efficacy for lower versus higher total and monthly dose

The results did not show a difference in efficacy between behaviourally based interventions delivered for less than the median dose versus more than the median dose, for both total and monthly clinician-delivered hours on ASD characteristics (see Figure 10 and Table 4). This confirms that efficacy of behaviourally based interventions on ASD characteristics does not differ between lower and higher hours of intervention.

Table 4. Results of analyses comparing lower and higher total and monthly doses for ASD

characteristics.

Figure 10a. Linear dose relationship for lower versus higher monthly clinician hours (based on median) for ASD characteristic outcomes.

Note: The green shaded area indicates less than the median number of monthly clinician-delivered hours, and the pink shaded area indicates higher than the median number of monthly clinician- delivered hours for this outcome.

Commented [GE18]: Alt text: Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for ASD characteristic outcomes. Coloured shading is used to differentiate between hours below and above the median number of hours. There is no change in Hedges’ g with increasing hours, with Hedges’ g remaining stable at approximately 0.25.,The 95% confidence interval widens with increasing monthly clinician- delivered hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Figure 10b. Linear dose relationship for lower versus higher total clinician hours (based on median) for ASD characteristic outcomes.

Note: The green shaded area indicates less than the median number of monthly clinician-delivered hours, and the pink shaded area indicates higher than the median number of monthly clinician- delivered hours for this outcome.

[Image not converted to Markdown – “Figure 10b” – check the source PDF page for the actual content]

Commented [GE19]: Alt text: Displays the linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for ASD characteristic outcomes. Coloured shading is used to differentiate between hours below and above the median number of hours. There is no change in Hedges’ g with increasing hours, with Hedges’ g remaining stable at approximately 0.25. The 95% confidence interval widens with increasing total clinician- delivered hours. Individual points are scattered greatly around the line of best fit. Much fewer data points are available for higher clinician-delivered hours. No data is available beyond 4000 hours. Values mentioned here are approximate.

Relationship between dose and change from baseline to follow-up separately within the intervention group and the comparison group

The lack of relationship between dose and effect size was corroborated by analyses exploring the relationship between dose and change from baseline to follow-up separately in both the intervention and comparison groups (see Figure 11). Neither those who received behaviourally based inventions nor those who received some level of clinician contact hours in the comparative group (through treatment as usual, e.g., speech pathology) benefited from more intensive doses or higher total contact hours with a clinician (see Tables B11 & B12 for model results).

The non-linear model suggested a gradual increase in effect beyond approximately 50 hours per month for the behaviourally based intervention group (Figure 11). However, confidence in this result is low due to a limited number of studies providing high intensity interventions. Individual linear and non-linear models with 95% confidence intervals are shown in Figures B15 & B19.

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Figure 11a. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for

change in ASD characteristic outcomes from pre- to post-intervention within intervention and comparison groups.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Linear model of monthly clinician-delivered dose by effect size” – check the source PDF page for the actual content]

Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group remains constant at 0.25 across hours. Hedges’ g for the intervention group increases from 0.25 at 0 hours to 0.8 at 150 hours. Values mentioned here are approximate.

Figure 11b. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in ASD characteristic outcomes from pre- to post-intervention within intervention and comparison groups.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Linear model of total clinician-delivered dose by effect size” – check the source PDF page for the actual content]

Displays the linear relationship between total clinician- delivered hours (from 0 to 3000 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group increases slightly from 0.2 at 0 hours to 0.4 at 2000 hours. Hedges’ g value for the intervention group increases remains stable at 0.45 across all hours. Values mentioned here are approximate.

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Figure 11c. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in ASD characteristic outcomes from pre- to post-intervention within intervention and comparison groups.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Figure 11c” – check the source PDF page for the actual content]

Displays the non-linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group increases from 0 at 20 hours to 0.4 at 50 hours, then decreases to 0.1 at 150 hours. Hedges’ g value for the intervention group increases from 0.1 at 0 hours to 0.5 at 20 hours, remains stable until 50 hours, then increases to 0.75 at 150 hours. Values mentioned here are approximate.

Figure 11d. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for change in ASD characteristic outcomes from pre- to post-intervention within intervention and comparison groups.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Figure 11d” – check the source PDF page for the actual content]

Displays the non-linear relationship between total clinician-delivered hours (from 0 to 3000 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group increases from 0 at 0 hours to 0.3 at 200 hours, then decreases to 0.1 at 800 hours, then increases to 0.5 at 2000 hours. Hedges’ g value for the intervention group increases from 0 at 0 hours to 0.75 at 100 hours, then gradually decreases to 0.5 at 3000 hours. Values mentioned here are approximate.

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5.6.2 Adaptive functioning

The linear models show a statistically significant relationship between increasing total and monthly clinician-delivered hours of intervention and better adaptive functioning outcomes following behaviourally based intervention (see Figure 12 and Table B10 for model estimates). Non-linear modelling demonstrated similar results (see Figure 12), with only slightly improved model fit. A visual investigation of the non-linear model suggested that adaptive function may require large doses to achieve a clinically meaningful effect size, with negligible effects shown at lower doses.

Although the linear dose response trends were statistically significant, the associated real-world impact of relatively smaller increases in intervention hours is minimal. Every increase of the intervention dose by 10 hours per month translates to an increase of only 3% of a standard deviation of the effect estimate. Similarly, every increase of 100 total hours of intervention overall translates to less than a 1% increase in standard deviation for the effect estimate.

Figure 12a. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for adaptive functioning outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Adaptive Functioning Outcomes” – check the source PDF page for the actual content]

Alt text: Displays the linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g for adaptive functioning outcomes. There is an upward trend, with Hedges’ g beginning just below 0 and ending at approximately 0.5. The 95% confidence interval widens slightly with increasing monthly clinician-delivered hours. Values mentioned here are approximate.

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Figure 12b. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for adaptive functioning outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Figure 12b” – check the source PDF page for the actual content]

Displays the non-linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for adaptive functioning outcomes. There is an upward trend, with Hedges’ g beginning at 0 and ending at approximately 0.5. The 95% confidence interval widens with increasing total clinician-delivered hours. Fewer data points are available for higher clinician-delivered hours. Values mentioned here are approximate.

Figure 12c. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for adaptive functioning outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Figure 12c” – check the source PDF page for the actual content]

Displays the non-linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for adaptive functioning outcomes. Hedges’ g values remain relatively stable from 0 to 100 hours, after which there is a gradual increase in Hedges’ g from approximately 0.1 to approximately 0.6. The 95% confidence interval is relatively wide. Values mentioned here are approximate.

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Figure 12d. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for adaptive functioning outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Figure 12d” – check the source PDF page for the actual content]

Commented [GE27]: Alt text: Displays the non-linear relationship between total clinician-delivered hours (from 0 to 5000 hours) and Hedges’ g for adaptive functioning outcomes. Hedges’ g values remain relatively stable from 0 to 1000 hours, at Hedges’ g of 0, after which there is an increase in Hedges’ g from approximately 0 to approximately 0.4 by 2500 hours, after which Hedges’ g remains stable again. The 95% confidence interval is relatively wide. Fewer data points are available for higher clinician-delivered hours. Values mentioned here are approximate.

Comparing efficacy for lower versus higher total and monthly dose

A difference in effect sizes was found between studies that delivered less than the median dose and those that delivered doses higher than the median, both for total clinician-delivered hours as well as hours per month (see Figure 13 for visual representation). Effect sizes were larger in studies that delivered doses at a higher versus lower dose, where a negligible effect size was found for the lower dose, and a small effect size for the higher dose. Practically, this suggests there are negligible effects below approximately 65 hours per month or 800 clinician hours overall.

However, there was not enough information (i.e., statistical power and precision) to detect a statistically significant difference between groups as only 10 and 8 studies, and 11 and 8 studies were available for analyses of both dose types. More detailed results can be found in Table 5.

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Table 5. Results of analyses comparing lower and higher total and monthly doses for adaptive functioning.

Figure 13a. Linear dose relationship for lower versus higher total and monthly clinician hours (based on median) for adaptive functioning outcomes.

Note: The green shaded area indicates less than the median number of monthly clinician-delivered hours, and the pink shaded area indicates higher than the median number of monthly clinician- delivered hours for this outcome.

Commented [GE28]: Alt text: Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for adaptive functioning outcomes. Coloured shading is used to differentiate between hours below and above the median number of hours. There is an upward trend, with Hedges’ g beginning just below 0 and ending at approximately 0.5. The 95% confidence interval widens slightly with increasing monthly clinician- delivered hours. Values mentioned here are approximate.

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Figure 13b. Linear dose relationship for lower versus higher total and monthly clinician

hours (based on median) for adaptive functioning outcomes.

Note: The green shaded area indicates less than the median number of monthly clinician-delivered hours, and the pink shaded area indicates higher than the median number of monthly clinician- delivered hours for this outcome.

[Image not converted to Markdown – “Image of Figure 13b” – check the source PDF page for the actual content]

Alt text: Displays the non-linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for adaptive functioning outcomes. Coloured shading is used to differentiate between hours below and above the median number of hours. There is an upward trend, with Hedges’ g beginning at 0 and ending at approximately 0.5. The 95% confidence interval widens with increasing total clinician-delivered hours. Fewer data points are available for higher clinician-delivered hours. Values mentioned here are approximate.

Relationship between dose and change from baseline to follow-up separately within the

intervention group and the comparison group

Analyses of the change from baseline to follow-up separately within the intervention and comparison groups provide some indication that the relationship between clinician time and effect size are specific to the intervention group. Linear models (see Figure 14 and Table B11) revealed a statistically significant relationship (albeit small) between increasing total and monthly clinician- delivered hours of intervention and better adaptive functioning outcomes at follow-up for the behaviourally based intervention group, while no such relationship was observed between clinician hours and change in outcomes from baseline to follow-up within the comparison groups (see Figure 14 and Table B12). Individual models with 95% confidence intervals are displayed in Figures B16 & B20.

Additionally, the non-linear models (Figure 14) indicate that improvements from baseline to follow- up within the behaviourally based intervention groups decrease after approximately 100 monthly hours. This may imply that the apparent increase in efficacy (comparing the intervention to the comparison group) with increasing dose (Figure 14) is driven by a decrease in effect within comparison groups rather than a specific benefit of more intervention hours for the behaviourally based intervention group. However, these results are based on a small number of studies and not all comparison group data are available, and therefore should be interpretated with caution.

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Figure 14a. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for

change in adaptive functioning outcomes from pre- to post-intervention in the intervention and comparison group.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.


Alt text: Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group decreases from 0.5 at 25 hours to 0.2 at 130 hours. Hedges’ g for the intervention group increases from 0 at 0 hours to 1.0 at 150 hours. Values mentioned here are approximate.

Figure 14b. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in adaptive functioning outcomes from pre- to post-intervention in the intervention and comparison group.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.


Alt text: Displays the linear relationship between total clinician- delivered hours (from 0 to 3000 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group increased from 0.1 at 0 hours to 0.8 at 2000 hours. Hedges’ g for the intervention group increased slightly from from 0.3 at 0 hours to 0.4 at 150 hours. Values mentioned here are approximate.

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Figure 14c. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in adaptive functioning outcomes from pre- to post-intervention in the intervention and comparison group.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Figure 14c” – check the source PDF page for the actual content]

Commented [GE32]: Alt text: Displays the non-linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group increases from 0 at 20 hours to 0.5 at 60 hours, then decreases to 0 at 125 hours. Hedges’ g value for the intervention group increases from -0.1 at 0 hours to 1.0 at 100 hours, then decreases to 0.1 at 150 hours. Values mentioned here are approximate.

Figure 14d. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for change in adaptive functioning outcomes from pre- to post-intervention in the intervention and comparison group.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Figure 14d” – check the source PDF page for the actual content]

Commented [GE33]: Alt text: Displays the non-linear relationship between total clinician-delivered hours (from 0 to 3000 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group remains stable at 0.2 from 0 to 500 hours, then decreases to -0.2 at 1250 hours, then increases to 1.1 at 2000 hours. Hedges’ g value for the intervention group increases from -0.5 at 0 hours to 1.1 at 1000 hours, then decreases to 0.8 at 3000 hours. Values mentioned here are approximate.

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5.6.3 Cognition and language

The linear models show a statistically significant relationship between increasing total and monthly clinician-delivered hours of intervention and better cognition and language outcomes following behaviourally based intervention as compared to a comparison group (see Figure 15). Linear model estimates are reported in Table B10. Non-linear modelling demonstrated similar results (see Figure 15), with only slightly improved model fit.

While the dose response trends for the linear models were statistically significant, similar to adaptive functioning outcomes, the associated real-world impact is of unclear clinical value. Every increase in dose of an additional 10 hours per month translates to an increase of 2% of a standard deviation of the effect estimate, and an increase in total dose of 100 additional intervention hours translates to less than a 1% increase. There is also less available evidence and thus less confidence (wider confidence intervals) in estimates for higher dose hours.

Figure 15a. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for cognition and language outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

![Displays the linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g for cognition and language outcomes. There is a slight upward trend, with Hedges’ g beginning at approximately 0.2 and ending at approximately 0.4. The 95% confidence interval widens with increasing monthly clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.]

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Figure 15b. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for cognition and language outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Figure 15b” – check the source PDF page for the actual content]

Commented [GE35]: Alt text: Displays the linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for cognition and language outcomes. There is an upward trend, with Hedges’ g beginning at approximately 0.25 and ending at approximately 0.5. The 95% confidence interval widens with increasing total clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Much fewer data points are available for higher clinician-delivered hours. Values mentioned here are approximate.

Figure 15c. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g)

for cognition and language outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Figure 15c” – check the source PDF page for the actual content]

Commented [GE36]: Alt text: Displays the non-linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for cognition and language outcomes. Hedges’ g remains relatively stable at 0.25 from 0 to 40 hours, after which there is an upward trend, with Hedges’ g beginning at approximately 0.2 and ending at approximately 0.4. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Figure 15d. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for cognition and language outcomes.

Note: Hedges’ g > 0 = better outcomes in the intervention group compared to the comparison group. Hedges’ g < 0 = better outcomes in the comparison group compared to the intervention group.

[Image not converted to Markdown – “Figure 15d” – check the source PDF page for the actual content]

Commented [GE37]: Alt text: Displays the non-linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for cognition and language outcomes. There is an upward trend, with Hedges’ g beginning at approximately 0.25 and ending at approximately 0.5. The 95% confidence interval widens with increasing total clinician-delivered hours. Individual points are scattered greatly around the line of best fit. Much fewer data points are available for higher clinician-delivered hours. Values mentioned here are approximate.

Comparing efficacy for lower versus higher total and monthly dose

The results did not show a difference in efficacy between the behaviourally based interventions delivered for less than the median dose of intervention versus more than the median dose of intervention, for both total and monthly clinician-delivered hours, on cognition and language outcomes (see Figure 16 and Table 6). This confirms that efficacy of behaviourally based interventions on cognition and language outcomes does not differ between lower and higher total and monthly clinician-delivered intervention hours.

Table 6. Results of analyses comparing lower and higher total and monthly doses for

cognition and language.

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Figure 16a. Linear dose relationship for lower versus higher monthly clinician hours (based

on median) for cognition and language outcomes.

Note: The green shaded area indicates less than the median number of monthly clinician-delivered hours, and the pink shaded area indicates higher than the median number of monthly clinician- delivered hours for this outcome.

[Image not converted to Markdown – “Linear dose relationship for lower versus higher monthly clinician hours” – check the source PDF page for the actual content]

Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for cognition and language outcomes. Coloured shading is used to differentiate between hours below and above the median number of hours.

There is a slight upward trend, with Hedges’ g beginning at approximately 0.2 and ending at approximately 0.4. The 95% confidence interval widens with increasing monthly clinician- delivered hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

Figure 16b. Linear dose relationship for lower versus higher total clinician hours (based on

median) for cognition and language outcomes.

Note: The green shaded area indicates less than the median number of monthly clinician-delivered hours, and the pink shaded area indicates higher than the median number of monthly clinician- delivered hours for this outcome.

[Image not converted to Markdown – “Linear dose relationship for lower versus higher total clinician hours” – check the source PDF page for the actual content]

Displays the linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for cognition and language outcomes. Coloured shading is used to differentiate between hours below and above the median number of hours.

There is an upward trend, with Hedges’ g beginning at approximately 0.25 and ending at approximately 0.5. The 95% confidence interval widens with increasing total clinician- delivered hours. Individual points are scattered greatly around the line of best fit. Much fewer data points are available for higher clinician-delivered hours. Values mentioned here are approximate.

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Relationship between dose and change from baseline to follow-up separately within the intervention group and the comparison group

Analyses of change from baseline to follow-up separately within the intervention and comparison groups suggest that the relationship dose and effect size is not specific to the intervention. Linear models (see Figure 17 and Tables B11 & B12) revealed statistically significant relationships between increasing total clinician hours and an improved outcomes from baseline to follow-up for both intervention and comparative groups, again with small coefficients (β = 0.0001 and 0.0002, respectively). Such relationships were not found for monthly clinician-delivered hours. This suggests that the slightly better outcomes with more hours are more likely to be related to amount of time spent with a clinician than the actual intervention taking place. The non-linear models (Figure 17) did not provide a clear indication of dose-response. Individual models with 95% confidence intervals can be seen in Figures B17 & B21.

Figure 17a. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in cognition and language outcomes from pre- to post-intervention in the intervention and comparison group.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

Displays the linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group increases from 0.2 at 20 hours to 0.5 at 150 hours. Hedges’ g value for the intervention group increases from 0.5 at 0 hours to 1.2 at 150 hours. Values mentioned here are approximate.

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Figure 17b. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for change in cognition and language outcomes from pre- to post-intervention in the intervention and comparison group.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Figure 17b” – check the source PDF page for the actual content]

Displays the linear relationship between total clinician- delivered hours (from 0 to 3000 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group increases from 0.2 at 100 hours to 0.5 at 2000 hours. Hedges’ g value for the intervention group increases from 0.6 at 0 hours to 1.0 at 3000 hours. Values mentioned here are approximate.

Figure 17c. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in cognition and language outcomes from pre- to post-intervention in the intervention and comparison group.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Figure 17c” – check the source PDF page for the actual content]

Displays the non-linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group is around 0.25 from 20 hours to 60 hours, where it then remains stable at 0.5 until 150 hours. Hedges’ g value for the intervention group decreases from 0.6 at 0 hours to 0.4 at 30 hours, then increases to 1.2 at 100 hours and decreases to 0.8 at 150 hours. Values mentioned here are approximate.

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Figure 17d. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in cognition and language outcomes from pre- to post-intervention in the intervention and comparison group.

Note: Hedges’ g > 0 = improvement in outcomes from baseline to follow-up in the specified group. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up in the specified group.

[Image not converted to Markdown – “Figure 17d” – check the source PDF page for the actual content]

Commented [GE43]: Alt text: Displays the non-linear relationship between total clinician- delivered hours (from 0 to 3000 hours) and Hedges’ g, separately for the intervention group and the comparison group. Hedges’ g value for the comparison group decreases from 0.25 at 0 hours to 0.15 at 1000 hours, then increases to 0.5 at 2000 hours. Hedges’ g value for the intervention group increases from 0.5 at 0 hours to 1.1 at 3000 hours. Values mentioned here are approximate.

5.7 Investigating the effect of population, intervention, and study

design factors on efficacy

Investigating the effect of population, intervention, and study design factors: summary of findings

• No notable differences in the efficacy of behaviourally based interventions were identified based on any investigated factors which includes the person delivering the intervention, intervention category, comparison group, age of children, primary intervention setting, and study design.
• There was no evidence to suggest that interventions delivered by a parent are inferior to those delivered by clinicians.
• There was no evidence to suggest that the type of comparison group (i.e., treatment as usual or non-behaviourally based, “eclectic” intervention) biases the results.
• There was no evidence to suggest that one choice of intervention characteristic is better than another. Interventions should be tailored to what is best for the child and their family’s unique circumstances and needs.

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5.7.1 Differences in effects within clinician, parent, or teacher-delivered interventions

The differences in effects when interventions were delivered either by a clinician, parent, or teacher was investigated using the evidence from the 98 studies (subgroup level data shown in Table 1). However, there was no evidence available for how teacher-delivered interventions effected family outcomes and adverse effects. Negligible effects were found in:

• teacher-delivered interventions for adaptive functioning outcomes;
• clinician-delivered interventions for family outcomes and adverse effects; and
• interventions which involved both clinician and parent-delivery on adverse effects.

Otherwise, consistent but small effects were identified for outcomes, regardless of the person delivering the behaviourally based intervention (see Table 7 and Figures B22-B26).

Importantly, there were no significant differences in efficacy of intervention for any outcome depending on who delivered the intervention. While not significant, there was a difference in effect size in favour of parent-delivered interventions compared to clinician-delivered interventions for reductions in adverse effects (i.e., parent stress).

Table 7. Subgroup analysis of person delivering intervention for each outcome domain

Note: This table contains pooled effect estimates (Hedges’ g) within each subgroup level for each of the five outcome domains. The colour of the text box indicates the direction of effect (worse outcomes for intervention versus comparison in pink; better outcomes for intervention versus comparison in green), with the opacity indicating the size of the effect (darker shade = stronger effect). Worse outcomes for the intervention versus comparison are also indicated by a negative effect size. Bold text and the presence of an asterisk (*) represents a significant effect estimate (p < 0.05). NA indicates insufficient data for analysis.

5.7.2 Differences in effects from each intervention category

Differences in effects relating to intervention categories (including behavioural, developmental, NDBI, technology-based, and TEACCH, as described in Section 4.1.3) were investigated. The number of studies which report data for each intervention category for each outcome domain are shown in Table 1. Results of this subgroup analysis are in Table 8 and detailed in Figures B22- B26.

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The evidence indicates no difference between intervention categories in efficacy across outcomes. Mostly small effect sizes in favour of behaviourally based interventions were seen, except where:

• a medium, positive effect size was observed for NDBI interventions on family outcomes;
• a negligible effect size was found for TEACCH and developmental interventions on cognition and language outcomes;
• a negligible effect size for technology-based and TEACCH interventions on ASD characteristics; and
• a small, but negative effect of TEACCH on adaptive functioning outcomes was identified, although this was not significant.

Overall, the findings show that intervention content or the theoretical underpinnings of behaviourally based interventions do not affect the efficacy of the intervention on outcomes.

Table 8. Subgroup analysis of intervention category for each outcome domain

Note: This table contains pooled effect estimates (Hedges’ g) within each subgroup level for each of the five outcome domains. The colour of the text box indicates the direction of effect (worse outcomes for intervention versus comparison in pink; better outcomes for intervention versus comparison in green), with the opacity indicating the size of the effect (darker shade = stronger effect). Worse outcomes for the intervention versus comparison are also indicated by a negative effect size. Bold text and the presence of an asterisk (*) represents a significant effect estimate (p < 0.05). NA indicates insufficient data for analysis.

5.7.3 Differences in effects by comparison group

Differences in effects relating to comparison group type (categories described in Section 4.1.3) were investigated. The number of studies which report data for each comparison group for each outcome domain are shown in Table 1. Results of this subgroup analysis are in Table 9 and detailed in Figures B22-B26.

The evidence indicates there is no difference between the type of comparison group including treatment as usual and eclectic in efficacy across outcomes. Mostly small effect sizes are seen, except for a negligible, negative effect size for behaviourally based interventions when compared to eclectic interventions on the reduction of adverse effects. Overall, there is no evidence that the type of comparison group affects the efficacy of the intervention on outcomes.

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Table 9. Subgroup analysis of comparison group for each outcome domain

Note: This table contains pooled effect estimates (Hedges’ g) within each subgroup level for each of the five outcome domains. The colour of the text box indicates the direction of effect (worse outcomes for intervention versus comparison in pink; better outcomes for intervention versus comparison in green), with the opacity indicating the size of the effect (darker shade = stronger effect). Worse outcomes for the intervention versus comparison are also indicated by a negative effect size. Bold text and the presence of an asterisk (*) represents a significant effect estimate (p < 0.05). NA indicates insufficient data for analysis.

5.7.4 Differences in effects by age group

Differences in effects relating to age group (0-1 years, 2-4 years, 5-7 years) were investigated. The number of studies which report data within each age group for each outcome domain are shown in Table 1. Results of this subgroup analysis are in Table 10 and detailed in Figures B22-B26.

The evidence shows no difference between age groups in efficacy across outcomes. Mostly small effect sizes are seen across age groups, except for:

• a medium effect size for 5-7 year-olds on cognitive outcomes;
• negligible effect sizes for 0-1 year-olds on ASD characteristics and adaptive functioning outcomes;
• a negligible effect size for 5-7 year-olds on adverse effects outcomes.

Overall, evidence indicates that age does not affect the efficacy of the intervention, although less studies currently report evidence for behavioural interventions for children aged less than 2 years old, and there is some indication of less benefit of intervention for ASD characteristic and adaptive functioning outcomes in this age group.

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Table 10. Subgroup analysis of age group for each outcome domain

Note: This table contains pooled effect estimates (Hedges’ g) within each subgroup level for each of the five outcome domains. The colour of the text box indicates the direction of effect (worse outcomes for intervention versus comparison in pink; better outcomes for intervention versus comparison in green), with the opacity indicating the size of the effect (darker shade = stronger effect). Worse outcomes for the intervention versus comparison are also indicated by a negative effect size. Bold text and the presence of an asterisk (*) represents a significant effect estimate (p < 0.05). NA indicates insufficient data for analysis.

5.7.5 Differences in effect by primary intervention setting

Differences in effects relating to primary setting of intervention (settings described in Section 4.1.3) were investigated. The number of studies that report data for each primary intervention setting and for each outcome domain are shown in Table 1. Results of this subgroup analysis are in Table 11 and detailed in Figures B22-B26.

Available evidence shows no significant difference in the efficacy of behavioural interventions across settings for most outcomes. However, there was a significant difference between behaviourally based interventions primarily delivered in health and home-based settings in their efficacy for family outcomes. It is likely that health settings are more efficacious (medium effect size) compared to home settings (small effect size) for this outcome domain. Importantly, there is still evidence that interventions primarily delivered in the home are efficacious for family outcomes, the evidence just indicates that they are efficacious to a lesser degree than interventions delivered in health settings.

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Table 11. Subgroup analysis of primary intervention setting for each outcome domain

Note: This table contains pooled effect estimates (Hedges’ g) within each subgroup level for each of the five outcome domains. The colour of the text box indicates the direction of effect (worse outcomes for intervention versus comparison in pink; better outcomes for intervention versus comparison in green), with the opacity indicating the size of the effect (darker shade = stronger effect). Worse outcomes for the intervention versus comparison are also indicated by a negative effect size. Bold text and the presence of an asterisk (*) represents a significant effect estimate (p < 0.05). NA indicates insufficient data for analysis.

5.7.6 Differences in effects by study design

Differences in effects relating to study design (study designs described in Section 4.1.3) were investigated. The number of studies which report data for each study design for each outcome domain are shown in Table 1. Results of this subgroup analysis are in Table 12 and detailed in Figures B22-B26.

Available evidence shows no significant difference in the efficacy of behavioural interventions across study designs for most outcomes. However, there was a significant difference between c cohort studies and randomised/non-randomised controlled trials on the reduction of adverse effects. It is likely that randomised and non-randomised controlled trials are more efficacious (small effect size) compared to cohort studies (small effect size), which demonstrate a significant increase in adverse effects (although with a small effect size).

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Table 12. Subgroup analysis of study design for each outcome domain

Note: This table contains pooled effect estimates (Hedges’ g) within each subgroup level for each of the five outcome domains. The colour of the text box indicates the direction of effect (worse outcomes for intervention versus comparison in pink; better outcomes for intervention versus comparison in green), with the opacity indicating the size of the effect (darker shade = stronger effect). Worse outcomes for the intervention versus comparison are also indicated by a negative effect size. Bold text and the presence of an asterisk (*) represents a significant effect estimate (p < 0.05). NA indicates insufficient data for analysis.

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6. Limitations

To the best of our knowledge, this report is the largest systematic review and meta-analysis of behavioural interventions in ASD conducted [4]. However, although 98 studies met eligibility criteria, approximately two thirds of these did not report quantifiable clinician-delivered hours of intervention. These studies were either parent- or teacher-delivered or did not report dose information at all. Therefore, the dose-response analyses in this report are based on a more limited evidence pool of 34 studies, which limits the precision (i.e., statistical power) of the analyses.

This report includes a statistical summary of best available evidence across the literature. In doing this, a high-level summary of over 4,500 children was made. Individual experiences and responses of children to these behaviourally based interventions will vary. It is beyond the scope of this investigation to evaluate philosophical or qualitative information around responses to behaviourally based interventions.

The quality and accuracy of reported dose information varied across studies. Often only the planned dose was reported rather than actual dose delivered. As actual dose delivered is commonly less than what was planned, the dose estimates reported here are likely an overestimation of what was delivered. Additionally, assessments of the potential risk of bias within included studies suggested a high, or serious, overall risk.

All children, even those in the comparison group, likely received some level of intervention. Studies varied in how comprehensively they reported details of alternative or standard-care intervention in the comparison group. Because of this, it was difficult to categorise and investigate the effects compared to different control groups. We acknowledge that the control group definitions used here (TAU and eclectic) are arbitrary, and there is potentially overlap across these groups.

Any behaviourally based interventions for children with ASD were included. While all-inclusive and comprehensive, this means that included interventions vary in their evidence for efficacy, with some based on stronger evidence. Additionally, fidelity was not adjusted for within analyses. The inclusion of all interventions, including those with less evidence and lower fidelity, may have resulted in an underestimation of the effect size.

Finally, the impact of important participant characteristics, such as autism severity, on the efficacy of behaviourally based interventions were unable to be assessed in this report. This was due to limited evidence and the lack of uniformity in the measures used to quantify autism severity within included studies.

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7. What did we learn?

7.1 What benefits are likely?

Overall, there is evidence for benefit, of a small effect size (extent of benefit), of behaviourally based interventions for children less than 7 years old with ASD for key clinical outcome domains. Better outcomes following behaviourally based interventions than those in comparative groups were identified for all five investigated outcome domains: ASD characteristics (e.g., socialisation, challenging behaviours), adaptive functioning, cognition and language, family outcomes, and in the reduction of adverse effects (i.e., child and parent stress/burden). Although, of note, the extent of the benefit (effect size) was smaller and more varied than reported in previous systematic reviews [4].

Importantly, while small in effect size, the benefit of behaviourally based interventions was found when compared against children undergoing both usual care and other “eclectic” intervention types. Importantly, larger improvements were consistently seen for children who underwent a behaviourally based intervention compared to children who experienced equivalent clinician-delivered hours (total and monthly) of treatment as usual or alternative, “eclectic” interventions. Due to limited data, this comparison of change following intervention by dose of intervention was only possible for ASD characteristics, adaptive functioning and cognition and language outcomes.

The small effect size and variability identified across outcomes means that benefit of behaviourally based interventions cannot be guaranteed across all interventions, settings, and participants. This indicates that multiple factors must be considered when making treatment decisions for a child with ASD and decisions pertaining to a child’s goals and family values should reflect their individual needs. This report has explored factors including dose, primary setting, the person delivering the intervention and child characteristics (e.g., age). The impact of these on the efficacy of behaviourally based interventions is discussed in the sections which follow. Of note, some important factors to consider (e.g., ASD severity) were unable to be investigated here.

7.2 Is more intervention better?

To answer the question of whether more clinician-delivered hours of intervention lead to better outcomes depends on the child’s outcome/s of interest (i.e., ASD characteristics, adaptive functioning, cognition and language).

Evidence shows that there is no benefit in increasing clinician hours (total or intensity) for ASD characteristic outcomes (e.g., global ASD measures, social affect, socialisation, challenging behaviours, etc). For this outcome, small benefits of behaviourally based interventions are consistently seen, regardless of dose. This means that lower dose intensities and total doses may be sufficient to see maximum benefit for ASD characteristics, and increased benefit is unlikely to occur with alterations of dose of the intervention.

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Contrastingly, evidence indicates that increasing both total clinician hours as well as the intervention intensity is associated with improved adaptive functioning and cognition and language outcomes. However, incremental increases (e.g., from 10 to 20 monthly hours) show little added value, so decisions about the amount of intervention received should be made upon a child’s progress towards their overall goals and what is most beneficial to the child, rather than dose alone. Improvements in cognition and language outcomes were shown at all dose levels, even low total clinician hours and dose intensities. This was not the case for adaptive functioning, where there is little evidence for benefit of behaviourally based interventions when delivered for less than approximately 800 hours, or 65 hours per month.

The results of the dose analyses warrant further validation due to the relatively small number of clinician-led studies reporting outcomes, especially at higher doses. The small number of studies meant there was large variability in estimates and lower confidence in the results, particularly for higher doses. Even if a dose relationship is found with more evidence, the potential benefit of increasing hours identified here was found to be minimal, translating to negligible real-world impact.

7.3 Who is best placed to deliver interventions?

Parent-delivered behaviourally based interventions (typically following parent training and ongoing s \upport from providers) may be as useful as clinician-delivered designs, with no difference found between these designs on benefit of the intervention. Support for the benefit of parent-delivered inerventions echoes the recent Autism CRC report (external), “Interventions for children on the a utism spectrum”, where the important and beneficial role of parents or caregivers in delivering early inerventions is highlighted [4], as well the National Guidelines for Best Practice in Early Childhood Intervention (external) [2], which emphasize the importance of family-centred supports and the involvement of family in the intervention process.

7.4 What other intervention design, implementation or participant

factors impact outcomes?

The current evidence does not indicate that the benefits of behaviourally based interventions differ based on the age group of children receiving the intervention, primary intervention setting (i.e., home or health setting), intervention category, type of comparison group (i.e., TAU or ‘eclectic’), or study design.

7.5 Considerations for practice

Behaviourally based interventions can be efficacious for children with ASD under 7 years. However, effects will vary depending on individual and intervention-specific factors. Importantly, no particular b enefit of specific intervention characteristics was found (e.g., intervention category, primary in tervention setting, person delivering the intervention). This implies that all factors may be useful in the right context and behaviourally based interventions can, and should be, individualised and take into consideration the needs, preferences, and individual circumstances of the child and their family.

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In clinician-led behaviourally based interventions, the number and intensity of clinician contact hours cannot, on its own, account for the variability in the effects found. This means that intervention planning decisions should consider dose, but not in the absence of considerations relating to the child’s goals, context, and family circumstances.

The specific goals of the participant and the planned outcomes of the intervention are of particular importance. Evidence presented here shows a benefit of more total hours as well as more intense intervention (more monthly hours of clinician-delivered intervention) for adaptive functioning and cognition and language outcomes. No impact of dose was found for ASD characteristic outcomes. This difference in the effect of dose based on outcomes measured indicates that the justification for increased dose intensity should be based upon the needs and the goals of the participant.

Evidence suggests that higher doses of behaviourally based intervention may be required to see benefit for adaptive functioning outcomes. It may be the case that if the goals which have prompted the child to seek intervention relate to adaptive functioning, at least 65 hours per month of intervention would achieve greatest benefit, and lower doses may be futile. It is important to note that this is an estimated amount and results will vary, based upon a child’s overall goals and other intervention-related factors. With such high doses required to see benefit for adaptive functioning, this may suggest that behaviourally based interventions may be less efficient (in terms of contact hours) for some outcomes. Thus, if treatment goal is adaptive functioning, participants may want to consider alternative approaches.

Additionally, the potential added benefits of incrementally increasing total and monthly dose across outcomes were shown to be minimal and unlikely to be clinically meaningful. Decisions to increase intervention intensity must be considered within the child’s context and dose should only be considered as one factor within treatment decisions as it is not always related to better outcomes. Importantly, decisions regarding the amount and duration of intervention should be made in consideration of concerns around the impact of intensive therapies on a child’s development (as highlighted within Recommendation 56 of the Autism CRC National Guideline [3]). For example, time spent in health and clinical settings may come at the cost of time for learning and development in more naturalistic settings that are family centred, which is beneficial to child development.

7.6 Potential areas and considerations for follow-up work

All analyses reported here are at group-level (grouped by characteristics within and between studies). To further this work, it is important to adjust for differences in individual circumstances. An analysis of individual participant data from the literature, clinical partners (e.g., from the Autism Specific Early Learning and Care Centres, ASELCCs [9]) or other NDIS providers will allow for factors relating to the interaction between dose and individual or intervention design factors to be explored in more detail.

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The robust evidence for efficacy of parent-led interventions warrants further investigation into the factors underlying efficacious interventions. Potential questions may include, among others:

• (1) what interventions or intervention components can be delivered effectively by parents,
• (2) how to balance clinician- and parent-time, and
• (3) how parents could be better supported to deliver interventions.

This can be achieved using network meta-analysis, which can investigate the components of interventions and synthetically compare intervention approaches head-to-head.

There is limited available information on enduring change over time following the conclusion of these interventions. Future research must include longer-term follow-ups in the children who receive these interventions in order to address this gap.

Importantly, research in this area is of poor quality. High risk of bias was identified in two thirds of studies included in this report. Improving the quality of studies in this field is vital. Common points for consideration in future study designs to improve study quality include ensuring the blinding of outcome assessors, concealing randomisation prior to assignment to intervention (for randomised designs), measuring or controlling for important confounders (e.g., age, autism severity, IQ), and using appropriate statistical methods for missing data.

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References

1. American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders - 5th Edition. 2013, Washington, DC: American Psychiatric Association.
2. Early Childhood Intervention Australia, National guidelines: Best practice in early childhood intervention. 2016: Sydney, NSW.
3. Trembath, D., et al., National guideline for supporting the learning, participation, and wellbeing of autistic children and their families in Australia. 2022, Autism CRC: Brisbane.
4. Whitehouse, A., Varcin, K., Waddington, H., Sulek, R., Bent, C., Ashburner, J., and V. Eapen, Goodall, E., Hudry, K., Roberts, J., Silove, N., Trembath, D., Interventions for children on the autism spectrum: A synthesis of research evidence. 2020: Brisbane.
5. Makrygianni, M.K. and P. Reed, A meta-analytic review of the effectiveness of behavioural early intervention programs for children with Autistic Spectrum Disorders. Research in Autism Spectrum Disorders, 2010. 4(4): p. 577-593.
6. Virues-Ortega, J., Applied behavior analytic intervention for autism in early childhood: meta-analysis, meta-regression and dose-response meta-analysis of multiple outcomes. Clin Psychol Rev, 2010. 30(4): p. 387-99.
7. Rodgers, M., et al., Interventions based on early intensive applied behaviour analysis for autistic children: a systematic review and cost-effectiveness analysis. Health Technol Assess, 2020. 24(35): p. 1-306.
8. Trembath, D., et al., An evidence-based framework for determining the optimal amount of intervention for autistic children. Lancet Child Adolesc Health, 2021. 5(12): p. 896-904.
9. Masi, A., et al., Clinical outcomes and associated predictors of early intervention in autism spectrum disorder: a study protocol. BMJ Open, 2021. 11(8): p. e047290.

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Appendix A: Detailed study methodology

This systematic review adheres to guidelines from the 2020 update of the Preferred Reporting Items for Systematic reviews and Meta-Analyses statement (PRISMA 2020 [1]).

A1. Study objectives

The review sought to synthesise the available evidence for the efficacy of behavioural interventions in children, aged 7-years or younger, with autism spectrum disorder (ASD).

The following objectives were examined in the current systematic review and meta-analysis:

1. What is the evidence for the general efficacy and effectiveness of behavioural interventions? a. What effect sizes should be expected on common composite and domain-specific assessments of ASD characteristics, functional or community outcomes? b. What adverse effects are reported and how common are they? c. How do effect sizes vary across outcome measures and domains? d. To what extent are any observed effects confounded by common sources of bias within and between studies?
2. How do effect sizes vary across settings? a. To what extent do effect sizes vary across studies (i.e., heterogeneity in true effects)? b. What common population, intervention and study design factors are possible moderators of heterogeneity?
3. How are effects associated with behavioural interventions related to intervention dose? a. What effects should be expected across different intensities (i.e., hours per week) and durations of interventions? b. How do intensity and duration interact across different delivery formats? c. What are the shapes of the dose-response curves for different outcomes? d. Are such dose-response relationships moderated or confounded by other design factors? e. How do the outcomes of each intervention compare to other behavioural interventions at different levels of intensity?

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A2. Electronic search strategy

A single search of MEDLINE, EMBASE, CENTRAL and PsycINFO via OVID was conducted on 15 November 2021 for studies examining the effects of behavioural interventions (based on ABA principles) in children aged 7-years or less with ASD on at least one outcome involving ASD characteristics, adaptive functioning, cognition and language, family outcomes, or adverse effects. The Medline search strategy is shown below.

The Ovid MEDLINE search strategy (including ALL from 1946 to November 15, 2021) was:

• exp Autism/ or exp Autistic Disorder/
• exp Autism Spectrum Disorder/ or exp Asperger Syndrome/
• (autis$ or Asperger$ or Kanner$ or ASD or ASC or AAC).ti,ab,kw.
• exp child development disorders, pervasive/
• exp Developmental Disabilities/
• Pervasive development$ disorder$.ti,ab,kw.
• 1 or 2 or 3 or 4 or 5 or 6
• exp Applied Behavior Analysis/
• exp Behavior Therapy/
• early intervention therap$.ti,ab.
• (high intensity adj2 (analys$ or behavior$ or behaviour$ or intervention$ or model$ or program$ or therap$ or treat$)).ti,ab.
• (low intensity adj2 (analys$ or behavior$ or behaviour$ or intervention$ or model$ or program$ or therap$ or treat$)).ti,ab.
• (intensive behavior$ adj2 (analys$ or intervention$ or model$ or program$ or therap$ or treat$)).ti,ab.
• (intensive behaviour$ adj2 (analys$ or intervention$ or model$ or program$ or therap$ or treat $)).ti,ab.
• (early behavior$ adj2 (analys$ or intervention$ or model$ or program$ or therap$ or treat$)).ti,ab.
• (early behaviour$ adj2 (analys$ or intervention$ or model$ or program$ or therap$ or treat $)).ti,ab.
• (comprehensive behavior$ adj2 (analys$ or intervention$ or model$ or program$ or therap$ or treat$)).ti,ab.
• (comprehensive behaviour$ adj2 (analys$ or intervention$ or model$ or program$ or therap$ or treat $)).ti,ab.
• `(applied behavior$ adj2 (analy$ or intervention$ or model$ or program$ or therap$ or treat$)).ti,ab.

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20. (applied behaviour$ adj2 (analy$ or intervention$ or model$ or program$ or therap$ or treat$)).ti,ab.
21. (ABA$ adj2 (analys$ or intervention$ or model$ or program$ or therap$ or treat$)).ti,ab.
22. (IBI or EIBI or ABA).ti,ab.
23. Lovaas$.mp.
24. discrete trial train$.ti,ab.
25. Picture exchange communication system$.ti,ab.
26. functional communication training$.ti,ab.
27. (intens$ adj2 (analys$ or behav$ or intervention$ or program$ or therap$ or treat$)).ti,ab,kw.
28. (behavio?r$ adj2 (analy$ or intervention$ or model$ or program$ or therap$ or treat$)).ti,ab,kw.
29. (behav$ adj2 (analy$ or intervention$ or model$ or program$ or therap$ or treat$)).ti,ab,kw.
30. Comprehensive application of behav* analysis to school*.mp.
31. (Comprehensive application of behav* analysis to school* or CABAS).ti,ab,kw.
32. PECS.ti,ab,kw.
33. Direct instruction$.ti,ab,kw.
34. “treatment and education of autistic and communication related handicapped children”.mp.
35. TEACCH.mp.
36. (Early Start Denver Model or ESDM).mp.
37. (Naturalistic Developmental behav* or NDBI*).mp.
38. (joint attention adj (training or skills or learning or intervention or program or therap$)).mp.
39. (Joint Attention Symbolic Play or JASPER).mp.
40. (Pivotal response adj1 (training or skills or learning or intervention or program or therap*)).mp.
41. reciprocal imitation.mp.
42. positive behav$ support.mp.
43. (developmental individual difference relationship based or DIR or floortime or floor time or interactive play).mp.
44. (developmental individual difference relationship or floortime or floor time or interactive play).mp.
45. (autism adj communication therapy).mp.
46. language training.mp.
47. Functional Communication Training.mp.
48. 8 or 9 or 10 or 11 or 12 or 13 or 14 or 15 or 16 or 17 or 18 or 19 or 20 or 21 or 22 or 23 or 24 or 25 or 26 or 27 or 28 or 29 or 30 or 31 or 32 or 33 or 34 or 35 or 36 or 37 or 38 or 39 or 40 or 41 or 42 or 43 or 44 or 45 or 46 or 47
49. 7 and 48

The search was not limited by time, location, or language. Articles written in a language other than English were translated. Additional articles were identified by scanning the reference lists of existing reviews. One reviewer (Nicole Hill) conducted the initial search. Screening of title and abstracts and the

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review of full texts was conducted by five reviewers (Nicole Hill, Ivana Randjelovic, Amit Lampit, Erica Ghezzi, Matthew McQueen). Each article was screened by two of the five reviewers. Discrepancies were resolved by Amit Lampit who also contacted corresponding authors for additional information when required.

A3. Study selection and eligibility criteria

A3.1 Types of studies

Eligible studies included in the meta-analysis were randomised or non-randomised. Eligible studies must have been published in peer-reviewed journals or included in previous systematic reviews, but data extracted from those studies may have been unpublished (e.g., obtained from study authors).

A3.2 Types of participants

Studies were eligible if they included children (mean age ≤7 years at baseline) with a diagnosis of ASD or reported as at high likelihood for ASD if too young for formal diagnosis (less than 3 years old). ASD comorbid with other conditions (including established or evident intellectual disability) will be eligible.

A3.3 Types of interventions

Behavioural interventions included those which:

 Used behaviourally based teaching strategies as the core components
 Used a comprehensive approach, to increase social engagement and learning while targeting a range of behaviours, skills (i.e., social, interpersonal, and daily living skills) and developmental domains (i.e., language, social communication, cognition, adaptive functioning, play development)
 Delivered face to face or using telehealth by qualified or trained individuals, on a one-to-one or small group basis to children directly, or via parents, caregivers, teachers, or combinations thereof
 Delivered at centre, home, school, or the community, or across multiple settings

Above criteria were based on Rodgers et al (2020) [2] and the Autism CRC [3]. Studies of eligible interventions combined with other approaches were included if ≥50% of intervention time met above criteria.

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Examples of typical interventions which meet the above criteria include:

• Early Intensive Behavioral Treatment (UCLA/Lovaas Model)
• Intensive ABA
• Non-intensive ABA
• Comprehensive Application of Behaviour Analysis to Schooling (CABAS)
• Verbal behavior
• Discrete trial training
• Direct Instruction
• Picture Exchange Communication System (PECS)
• Treatment and Education of Autistic and Communication related handicapped Children (TEACCH)
• Early Start Denver Model (ESDM)
• Comprehensive intensive early intervention
• Naturalistic Developmental Behavioural Interventions (NDBIs)
• Early Social Interaction Project
• Joint attention and imitation skill-building
• Joint Attention, Symbolic Play, Engagement, and Regulation (JASPER)
• Learning Experiences Alternative Program (LEAP)
• Pivotal Response Training (PRT, also called Pivotal Response Treatment)
• Reciprocal Imitation Training
• Positive Behaviour Support
• Developmental Individual-Difference Relationship-Based (DIR) / Floortime
• Paediatric Autism and Communication Therapy (PACT)
• Language training
• Functional Communication Training

Eligibility of behavioural interventions were determined in consultation with Megan Clark, Postdoctoral Research Fellow and Provisional Psychologist at the Olga Tennison Autism Research Centre of La Trobe University.

There will be no limitation on intervention dose or intensity (hours per week, total number of hours, overall duration). All eligible intervention arms in multi-arm studies will be included.

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A3.4 Types of comparators

Studies had to report data for at least one comparison group which was also comprised of children less than 7 years with ASD. Eligible comparisons include passive/waitlist control, treatment as usual (TAU), alternative community-based interventions (e.g., eclectic treatments) or non-evidence supported treatments.

A3.5 Types of outcome measures

Outcomes assessed at two time points (before and after the intervention) were eligible. Eligible outcomes included any measure which came under the following five categories:

ASD characteristics: Describes specific characteristics of autism, as well as global ASD characteristic measures. Includes characteristics such as emotion regulation, restricted repetitive behaviours, sensory problems, social affect, socialisation, challenging behaviours.

Cognition and language: Describes the child’s cognitive and language abilities. Includes measures of IQ, developmental age, motor skills, as well as receptive and expressive language.

Adaptive functioning: Describes measures of the child’s everyday functioning. Includes functional behaviours such as toileting, helping with chores, answering the phone.

Family outcomes: Describes wellbeing or quality of life of the child, caregiver, or overall family unit, as well as parent sense of competency.

Adverse effects: Describes adverse effects of the intervention. Includes child distress (e.g., anxiety/depression) as well as parent stress or burden. These effects were coded so that higher scores indicated better outcomes (i.e., reduction in adverse effects).

A4. Data collection and coding

Coding of outcome measures was conducted by Erica Ghezzi who double-checked all data for accuracy. Data was coded into an excel spreadsheet for analysis in R. Data from studies were usually Entered as means and standard deviations for pre-post measures for the intervention and control group, but if this was unavailable, any data from which an effect size for the difference between intervention and control groups in change from pre- to post-measures could be calculated was entered.

If a study included multiple follow-up timepoints during the intervention (e.g., after 1 year of intervention AND after 2 years of intervention), both were collected as they represent different dose amounts for the dose analysis. If a study had multiple follow-up points (with no further intervention delivered), data from the first time-point, immediately after completion of the intervention (maximum dose) was collected.

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In addition to the primary outcome measures, information on the study design and characteristics were extracted for each eligible article which included, author, publication year, country, study design, intervention description, control description, participant characteristics (e.g., age, gender), intervention settings, intervention dose (duration and frequency), mode of delivery (e.g., parent or clinical supervised).

A5. Assessing the quality of the evidence

Risk of bias was assessed using the Cochrane RoB 2.0 [4] for included studies which were randomised controlled trials, and the ROBINS-I [5] tool for included studies which employed non-randomised designs (e.g., non-randomised controlled trials, cohort studies).

Risk of bias was assessed for each reported outcome domain (e.g., ASD characteristics, adaptive functioning, etc) within each included manuscript. Risk assessments were then summarised at the study level by taking the highest risk assessment for each risk of bias domain. Overall assessments were made as per the respective risk of bias tool’s guidelines.

A6. Data analysis

All analyses were conducted using the R packages metafor and robumeta.

A6.1 Combining effects from included studies

The primary outcome was standardised mean difference (calculated as Hedges’ g) of difference between intervention and control groups in change from pre- to post-intervention. Precision of the Hedges’ g was calculated for each outcome measure by the 95% confidence interval (CI). A positive Hedges’ g implies better therapeutic effects over time in the intervention group compared to the control group. By convention, Hedges’ g values of 0.2, 0.5 and 0.8 are considered small, moderate or large effect sizes, respectively.

When studies provided multiple effect sizes or subgroups, all eligible effect sizes and subgroups were poolled using robust variance estimation models. Heterogeneity across studies was quantified using the tau2 statistic.

Small-study effect (‘publication bias’) was assessed by visually inspecting funnel plots of effect sizes versus standard error. Where there were at least 10 studies in analyses, the small-study effect was formally tested using Egger’s test. If evidence was found for this effect (if p < 0.1), the trim and fill method was used to create an adjusted effect estimate.

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A6.2 Dose response analyses

Relationship between dose and efficacy

Linear models

The relationship between dose and effect size was modelled using multivariate linear meta-regression. When studies provided multiple effect sizes for the same dose of intervention, all eligible effect sizes and subgroups were pooled using robust variance estimation models, and then a linear regression was run.

These models were run separately for the two measures of dose (total clinician-delivered hours, and monthly clinician-delivered hours) for each of the three outcome domains which reported sufficient data for dose analyses (ASD characteristics, adaptive functioning, cognition and language).

The model statistics were recorded, and the model significance was tested using the p-value (p < 0.05 represents a statistically significant lineal model). The model was then plotted, including the 95% confidence interval, which represents the precision of the model.

Non-linear models

The same relationships (effect size and dose for relevant outcome domains) were then explored using non-linear meta-regression models. This involved the same process, except now the relationship was not assumed to be linear.

For each analysis of dose and effect size by outcome domain, three types of non-linear models were investigated: cubic polynomial, restricted cubic spline, and thin plate spline. Across outcomes, the restricted cubic spline model was shown to have the best fit, and so this method was used for all non- linear models within this report.

Once again, model statistics were recorded, and the model significance was tested using the p-value (p < 0.05 represents a statistically significant lineal model). Plots of non-linear models included the 95% confidence interval to represent model precision.

Comparing efficacy for lower versus higher total and monthly dose

This analysis involved pooling the effect sizes (Hedges’ g), as was done the main analysis described above, but now within subgroups. In this case, the subgroups were lower and higher dose, split by the median dose across studies with available data within each outcome domain (ASD characteristics, adaptive functioning, and cognition and language). Separate analyses were conducted for the two definitions of dose: total clinician-delivered hours and monthly clinician-delivered hours (dose intensity).

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Differences in effect size between lower versus higher dose levels were assessed for each dose type and outcome domain using a Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. In this case, it would mean there is a difference in efficacy for that outcome domain between lower and higher dose levels (per the definition of the specified analysis).

These analyses supplement and corroborate the previous dose analysis (Relationship between dose and efficacy). While the previous analyses investigated the relationship of dose and efficacy by investigating dose as a continuous variable, these analyses treat dose as a dichotomous variable (lower versus higher, defined based on a median split).

Relationship between dose and change from baseline to follow-up separately within the intervention group and the comparison group

The linear and non-linear model analysis is identical to the Relationship between dose and efficacy analysis described above. However, different data is input for the effect size (Hedges’ g) and the interpretation differs.

In this case, Hedges’ g represents the change between two time-points: baseline (pre-intervention) and follow-up. This effect size was calculated separately for the behaviourally based intervention group and the comparison group. As such, linear and non-linear models of dose by effect size were conducted separately for behavioural intervention and comparison groups.

Dose once again was defined as clinician-delivered hours (total and monthly). Studies were only included in the analyses if dose was reported. For the comparison group, the study had to report clinician-delivered hours of alternative intervention (including those in the community, occupational therapy, speech pathology, etc).

To compare the difference from baseline to follow-up across dose (total and monthly clinician-delivered hours) between behaviourally based intervention and comparison groups, plots were created. These plots included the linear and non-linear models (without 95% confidence intervals) for behaviourally based intervention and comparison groups on the same plot. This was to allow for comparison of effect sizes between the two groups for the same dose amount.

It is important to note that, while plotted in this way, different studies contributed to each analysis (less studies in the comparison group analyses). Additionally, even if studies contributed to both, the dose amount between the behaviourally based intervention group and the comparison group is not necessarily equal.

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A6.3 Subgroup analyses

Heterogeneity (variance between studies) was further investigated through subgroup analyses. These involved pooling the effect sizes (Hedges’ g), as was done the main analysis described above (beneath the Data Analysis header), but now within subgroups. Subgroup analyses assess whether there were any differences in efficacy of behaviourally based interventions based on differences in study design, intervention characteristics, and population characteristics.

All seven subgroups (low versus high dose [outlined in the previous section], person delivering, intervention category, comparison group, age group, primary intervention setting, and study design) were assessed for each of the five outcome domains. Each subgroup level that two or more studies reported data for a particular outcome domain was included in analyses.

Pooled effect sizes (Hedges’ g) and confidence intervals were estimated for each subgroup level individually. Differences in effect size between subgroup levels was then assessed using a Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup.

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Appendix B: Results

B1. Study selection

The initial search identified 11802 records, of which 10 were duplicates. A total of 11792 records were screened based on title an abstract (Figure B1). The full-text of 816 records were assessed, of which 113 records met the eligibility criteria. Of 15 articles identified through citation searching, 7 met eligibility criteria. After removing three articles as outliers, 117 records were included in analyses. A total of 35 records reported data from the same overarching study as at least one other record. These 35 records were combined within studies, to form 14 independent studies. The final dataset included 98 independent studies (14 of which included multiple records).

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Figure B1. Summary of study selection

Commented [GE44]: Alt-text: This figure summarises the process of study selection for the systematic search. A total of 11802 studies were identified from the database search, and 11792 were available after duplicates were removed. After title and abstract screening, 828 full text articles were assessed for eligibility (1 was unable to be retrieved, 1 was unable to be translated). A total of 713 articles were excluded for the following reasons:

• 222 wrong study design
• 109 wrong patient population
• 134 reviews
• 85 no usable data
• 70 wrong intervention
• 61 abstract/registration/dissertation
• 20 wrong outcomes
• 5 duplicates
• 4 identical data already reported
• 2 erratum

A total of 113 articles identified via database searching met eligibility criteria, although 3 of these were excluded as outliers.

A further 15 records were identified through citation searching and were assessed for eligibility. Of these, 8 were excluded for the following reasons:

• 3 wrong intervention
• 3 wrong study design
• 1 no usable data
• 1 wrong patient population

A total of 7 articles originally identified via citation searching met eligibility criteria.

A combined total of 117 articles representing 98 independent studies were included in this meta-analysis.

B2. Characteristics of included studies

A total of 117 records representing 98 studies were included in the meta-analysis. Details of all included records are shown in Table B1. All studies included interventions based on behaviourally based principles, but the characteristics of the interventions varied largely across studies (see Section 4.1.2 of the main report). The intensity and duration of behaviourally based behavioural interventions ranged between 2.2 to 157.53 clinician-delivered hours per month, delivered over 4 to 141 weeks.

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Table B1: Description of included studies

| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 1 | Argumedes 2021 [6] | Canada | Random | 23 | 78 | 51.6 | ASD characteristics | Behavioural | TAU | Home | Parent delivered | | 2 | Azarbehi 2012 [7] | Canada | Cohort | 20 | 85 | 41 | ASD characteristics, adaptive functioning, cognition | Behavioural | TAU | Home | Clinician | | 3 | Iadarola 2018 [8] | USA | Random | 180 | 87.78 | 50.4 | ASD characteristics, family outcomes, adverse effects | Behavioural | Eclectic | Home | Parent delivered | | 3 | Bearss 2015 [9] | USA | Random | 180 | 87.78 | 56.4 | ASD characteristics | Behavioural | Eclectic | Health | Parent delivered | | 3 | Scahill 2016 [10] | USA | Random | 180 | 87.78 | 56.4 | Adaptive functioning | Behavioural | Eclectic | Health | Parent delivered | | 4 | Bentenuto 2020 [11] | Italy | Non-random | 37 | NR | 41.6 | ASD characteristics, cognition | NDBI | TAU | Health | Clinician | | 5 | Bernard-Opitz 2004 [12] | Singapore | Non-random | 8 | NR | 38.75 | ASD characteristics | Behavioural | Eclectic | Health | Clinician and parent | | 6 | Blackman 2020 [13] | USA | Non-random | 12 | 66.67 | 48.48 | ASD characteristics, family outcomes, adverse effects | Behavioural | TAU | Health | Parent delivered | | 7 | Bordini 2020 [14] | Brazil | Random | 66 | 80.3 | 57.6 | ASD characteristics, adaptive functioning, cognition | Behavioural | TAU | Health | Parent delivered | | 8 | Boyd 2014 [15] | USA | Cohort | 113 | 83.19 | 48.24 | ASD characteristics, adaptive functioning, cognition | Other | TAU | Early education | Teacher delivered |

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| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 8 | Coman 2014 [16] | USA | Cohort | 144 | 85.42 | 48.36 | ASD characteristics, adaptive functioning, cognition | TEACCH | TAU | Early education | Teacher delivered | | 9 | Cariveau 2019 [17] | USA | Random | 10 | NR | 34.84 | ASD characteristics, cognition | Behavioural | TAU | Health | Clinician | | 10 | Carr 2007 [18] | UK | Non-random | 10 | NR | 69.6 | ASD characteristics, cognition | Behavioural | TAU | Early education | Clinician | | 11 | Chang 2016 [19] | USA | Random | 66 | 89 | 50.26 | ASD characteristics, cognition | NDBI | TAU | Early education | Teacher delivered | | 12 | Charman 2021 [20] | England | Random | 62 | 80.6 | 80.04 | ASD characteristics, family outcomes, adverse effects | Behavioural | TAU | Home | Parent delivered | | 13 | Chiang 2016 [21] | Taiwan | Non-random | 34 | NR | 37.6 | ASD characteristics | Developmental | TAU | Health | Clinician | | 14 | Cohen 2006 [22] | USA | Non-random | 42 | 83.33 | NR | Adaptive functioning, cognition | Behavioural | TAU | Home | Clinician | | 15 | Coleman 2017 [23] | USA | Random | 19 | 81.5 | 45.4 | ASD characteristics | Behavioural | Eclectic | Home | Parent delivered | | 16 | Colombi 2018 [24] | Italy | Non-random | 92 | NR | 34.22 | ASD characteristics, adaptive functioning, cognition | NDBI | TAU | Health | Clinician | | 17 | D’Elia 2014 [25] | Italy | Non-random | 30 | 80 | 49.2 | ASD characteristics, adaptive functioning, cognition, adverse effects | TEACCH | TAU | Early education | Clinician and parent | | 18 | Dai 2018 [26] | Albania | Non-random | 29 | 89.66 | 45.17 | Family outcomes | Behavioural | TAU | Home | Parent delivered |

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| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 19 | Dawson 2010 [27] | USA | Random | 48 | NR | 23.5 | ASD characteristics, adaptive functioning, cognition | NDBI | TAU | Health | Clinician and parent | | 19 | Estes 2015 [28] | USA | Random | 48 | 76.92 | 23.5 | ASD characteristics, adaptive functioning | NDBI | NA | Health | Clinician and parent | | 20 | Dixon 2019 [29] | USA | Non-random | 20 | 84.21 | 65.4 | Cognition | Behavioural | TAU | Health | Clinician | | 21 | Drew 2002 [30] | England | Random | 24 | 79.17 | 22.5 | ASD characteristics, cognition, adverse effects | Developmental | TAU | Home | Parent delivered | | 22 | Duifhuis 2017 [31] | Netherlands | Non-random | 47 | 83.33 | 69.48 | ASD characteristics, adverse effects | NDBI | TAU | Home | Clinician and parent | | 23 | Eikeseth 2002 [32] | Norway | Non-random | 25 | 76 | 65.68 | Adaptive functioning, cognition | Behavioural | Eclectic | Early education | Clinician and parent | | 24 | Eikeseth 2012 [33] | Norway | Non-random | 59 | 83.05 | 49.2 | Adaptive functioning | Behavioural | Eclectic | Early education | Clinician | | 25 | Eldevik 2006 [34] | Norway | Cohort | 28 | 85.71 | 50.86 | ASD characteristics, adaptive functioning, cognition | Behavioural | Eclectic | Early education | Clinician and parent | | 26 | Eldevik 2010 [35] | Norway | Cohort | 25 | 76 | 49.64 | Adaptive functioning, cognition | Behavioural | Eclectic | Early education | Clinician | | 27 | Eldevik 2012 [36] | Norway | Cohort | 43 | 76.74 | 43.32 | Adaptive functioning, cognition | Behavioural | Eclectic | Early education | Clinician | | 28 | Elder 2012 [37] | USA | Random | 97 | 77 | 21 | ASD characteristics, family outcomes | NDBI | TAU | Health | Parent delivered | | 28 | Estes 2014 [38] | USA | Random | 82 | 75.61 | 21.01 | Adverse effects | NDBI | TAU | Health | Parent delivered |

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| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 29 | Fava 2011 [39] | Italy | Non-random | 22 | 86.36 | 48.23 | ASD characteristics, adaptive functioning, cognition, adverse effects | Behavioural | Eclectic | Home | Clinician and parent | | 30 | Felzer-Kim 2020 [40] | USA | Random | 14 | 71.43 | 53.86 | Cognition | Behavioural | Eclectic | Health | Clinician | | 31 | Feng 2019 [41] | China | Non-random | 67 | 78.46 | 28.46 | ASD characteristics | NDBI | TAU | Health | Clinician | | 32 | Flanagan 2011 [42] | Canada | Cohort | 134 | NR | 35.2 | ASD characteristics, adaptive functioning | Behavioural | TAU | Community | Clinician | | 33 | Fox 2018 [43] | USA | Random | 10 | 70 | 32.8 | ASD characteristics, cognition, family outcomes, adverse effects | NDBI | TAU | Health | Parent delivered | | 34 | Frey 2015 [44] | USA | Random | 34 | NR | 49.2 | ASD characteristics, adaptive functioning | Behavioural | TAU | Early education | Clinician and parent | | 35 | Furukawa 2018 [45] | Japan | Non-random | 21 | 81 | 62.88 | ASD characteristics, adverse effects | Other | TAU | Health | Parent delivered | | 36 | Gengoux 2019 [46] | USA | Random | 43 | 88.37 | 48.43 | ASD characteristics, adaptive functioning, cognition | NDBI | TAU | Home | Clinician and parent | | 37 | Gengoux 2021 [47] | USA | Random | 44 | 95.45 | 60 | ASD characteristics, adaptive functioning | Developmental | TAU | Health | Clinician | | 38 | Ginn 2017 [48] | USA | Random | 30 | 80 | 56.4 | ASD characteristics, cognition, adverse effects | Other | TAU | Health | Parent delivered | | 39 | Gomes 2019 [49] | Brazil | Cohort | 33 | 87.5 | 59.3 | Adaptive functioning, cognition | Behavioural | TAU | Home | Parent delivered |

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| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 40 | Goods 2013 [50] | USA | Random | 15 | NR | 51.9 | ASD characteristics, cognition | NDBI | TAU | Early education | Clinician | | 41 | Grahame 2015 [51] | UK | Random | 45 | 86.67 | 61.47 | ASD characteristics, cognition, family outcomes | Behavioural | TAU | Health | Parent delivered | | 42 | Grindle 2012 [52] | UK | Cohort | 29 | 86.21 | 61.7 | Adaptive functioning, cognition | Behavioural | TAU | Early education | Clinician and parent | | 43 | Gulsrud 2019 [53] | USA | Random | 20 | 65 | 49.36 | ASD characteristics, cognition | NDBI | TAU | Early education | Clinician | | 44 | Haglund 2021 [54] | Sweden | Non-random | 94 | 81.91 | 51.6 | ASD characteristics | NDBI | TAU | Early education | Clinician and parent | | 45 | Hampton 2020 [55] | USA | Random | 68 | 77.94 | 43 | ASD characteristics, cognition | NDBI | TAU | Health | Clinician and parent | | 46 | Haraguchi 2020 [56] | Japan | Non-random | 61 | 85.25 | 46.75 | ASD characteristics, adaptive functioning, cognition, adverse effects | Behavioural | TAU | Health | Clinician and parent | | 47 | Hardan 2015 [57] | USA | Random | 48 | 75 | 49.2 | ASD characteristics, adaptive functioning, cognition | NDBI | TAU | Health | Parent delivered | | 48 | Ho 2020 [58] | Taiwan | Random | 24 | 100 | 48.5 | ASD characteristics, adaptive functioning, cognition | Developmental | TAU | Home | Parent delivered | | 49 | Holzinger 2019 [59] | Austria | Non-random | 13 | 100 | 43.32 | ASD characteristics, adaptive functioning, cognition, family outcomes, adverse effects | NDBI | TAU | Home | Clinician | | 50 | Howard 2005 [60] | USA | Non-random | 45 | 84.44 | 32.17 | Adaptive functioning, cognition | Behavioural | TAU | Early education | Clinician and parent |

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| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 50 | Howard 2014 [61] | USA | Non-random | 45 | NR | 32.24 | Adaptive functioning, cognition | Behavioural | TAU | Early education | Clinician and parent | | 51 | Iadarola 2018 [62] | USA | Random | 150 | 87.33 | 85.14 | ASD characteristics, adaptive functioning | Behavioural | TAU | Early education | Teacher delivered | | 52 | Ingersoll 2010 [63] | USA | Random | 21 | 85.71 | 39.38 | ASD characteristics | NDBI | TAU | Health | Clinician | | 52 | Ingersoll 2012 [64] | USA | Random | 27 | 88.89 | 37.95 | ASD characteristics | NDBI | TAU | Health | Clinician | | 53 | Johnson 2019 [65] | USA | Random | 42 | 95.24 | 61.2 | ASD characteristics, adaptive functioning, family outcomes, adverse effects | Behavioural | TAU | Health | Parent delivered | | 54 | Jouen 2017 [66] | France | Non-random | 24 | 100 | 83.76 | ASD characteristics, adaptive functioning, cognition, adverse effects | Technology-based | TAU | Health | Clinician and parent | | 55 | Kaale 2012 [67] | Norway | Random | 61 | 78.69 | 48.8 | ASD characteristics | Developmental | TAU | Early education | Teacher delivered | | 55 | Kaale 2014 [68] | Norway | Random | 61 | 78.69 | 48.8 | ASD characteristics, cognition | Developmental | TAU | Early education | Teacher delivered | | 56 | Arora 2008 [69] | USA | Random | 36 | NR | 42.62 | ASD characteristics | Developmental | TAU | Early education | Clinician | | 56 | Kasari 2006 [70] | USA | Random | 38 | 81.08 | 42.34 | ASD characteristics | Developmental | TAU | Early education | Clinician | | 56 | Kasari 2008 [71] | USA | Random | 38 | 81.08 | 42.34 | ASD characteristics, cognition | Developmental | TAU | Early education | Clinician |

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| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 56 | Lawton 2012 [72] | USA | Random | 36 | 78.12 | 41.36 | ASD characteristics | Developmental | Eclectic | Early education | Clinician | | 57 | Kasari 2010 [73] | USA | Random | 38 | 76.32 | 30.83 | ASD characteristics | Developmental | TAU | Health | Parent delivered | | 58 | Kasari 2015 [74] | USA | Random | 86 | 81 | 31.5 | ASD characteristics, cognition, adverse effects | NDBI | Eclectic | Health | Parent delivered | | 58 | Schlink 2022 [75] | USA | Random | 86 | 81 | 31.5 | Adverse effects | NDBI | Eclectic | Health | Parent delivered | | 58 | Gulsrud 2016 [76] | USA | Random | 86 | 81 | 31.5 | Family outcomes | NDBI | Eclectic | Health | Parent delivered | | 58 | Dimachkie 2021 [77] | USA | Random | 75 | 82.7 | 31.5 | ASD characteristics | NDBI | Eclectic | Health | Parent delivered | | 59 | Lawton 2012 [78] | USA | Random | 16 | NR | 44.69 | ASD characteristics | NDBI | TAU | Early education | Teacher delivered | | 60 | Leaf 2017 [79] | USA | Random | 15 | NR | 56.4 | ASD characteristics | Behavioural | TAU | Health | Clinician | | 61 | Magiati 2007 [80] | UK | Cohort | 44 | NR | 39.64 | ASD characteristics, adaptive functioning, cognition | Behavioural | Eclectic | Home | Parent delivered | | 62 | Manohar 2019 [81] | India | Random | 50 | 88 | 41.4 | ASD characteristics, family outcomes, adverse effects | NDBI | TAU | Home | Parent delivered | | 63 | Matthews 2018 [82] | USA | Non-random | 18 | 97.22 | 40.78 | ASD characteristics, family outcomes, adverse effects | Behavioural | TAU | Health | Parent delivered | | 64 | Nojiri 2019 [83] | Japan | Random | 36 | 86.11 | 43.4 | ASD characteristics, family outcomes | Behavioural | TAU | Health | Parent delivered |

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| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 65 | Nowell 2019 [84] | USA | Random | 17 | 76.47 | 81.84 | ASD characteristics, family outcomes | TEACCH | TAU | Health | Clinician and parent | | 66 | Oosterling 2010 [85] | The Netherlands | Non-random | 67 | 77.61 | 34.32 | ASD characteristics, cognition, family outcomes | Developmental | TAU | Home | Parent delivered | | 67 | Pajareya 2011 [86] | Thailand | Random | 32 | 87.5 | 54.05 | ASD characteristics, adaptive functioning | Developmental | TAU | Home | Parent delivered | | 68 | Peters-Scheffer 2013 [87] | The Netherlands | Cohort | 40 | 90 | 62.52 | ASD characteristics, adaptive functioning, cognition, adverse effects | Behavioural | TAU | Early education | Clinician | | 69 | Reitzel 2013 [88] | Canada | Random | 15 | NR | 58.5 | ASD characteristics, adaptive functioning, family outcomes, adverse effects | Behavioural | TAU | Health | Clinician | | 70 | Remington 2007 [89] | England | Cohort | 44 | NR | 36.99 | ASD characteristics, adaptive functioning, cognition, family outcomes, adverse effects | Behavioural | TAU | Home | Clinician and parent | | 70 | Kovshoff 2011 [90] | England | Cohort | 44 | NR | 36.99 | ASD characteristics, adaptive functioning, cognition | Behavioural | TAU | Home | Clinician and parent | | 71 | Rogers 2006 [91] | USA | Random | 10 | 100 | 38.4 | Cognition | NDBI | Eclectic | Health | Clinician and parent | | 72 | Rogers 2019 [92] | USA | Random | 118 | 77.97 | 21.02 | ASD characteristics, adaptive functioning, cognition | NDBI | TAU | Home | Parent delivered | | 72 | Rogers 2012 [93] | USA | Random | 98 | 77.55 | 20.98 | ASD characteristics, adaptive functioning, cognition | NDBI | TAU | Health | Parent delivered |

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| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 73 | Rogers 2014 [94] | USA | Non-random | 14 | 63.64 | 9 | ASD characteristics, cognition | NDBI | TAU | Health | Parent delivered | | 74 | Ruiz 2020 [95] | USA | Random | 40 | 97.5 | 60.38 | ASD characteristics | NDBI | TAU | Community | Clinician | | 75 | Shawler 2017 [96] | USA | Random | 51 | 86.27 | 27.69 | Cognition | Behavioural | TAU | NA | Clinician and parent | | 76 | Sheinkopf 1998 [97] | USA | Cohort | 22 | NR | 34.55 | ASD characteristics, cognition | Behavioural | TAU | Home | Clinician | | 77 | Shire 2017 [98] | USA | Random | 113 | 77.88 | 31.63 | ASD characteristics | NDBI | TAU | Community | Teacher delivered | | 78 | Sinai-Gavrilov 2020 [99] | Israel | Non-random | 51 | 82.35 | 44.37 | Adaptive functioning, cognition | NDBI | Eclectic | Early education | Teacher delivered | | 79 | Solomon 2014 [100] | USA | Random | 128 | 82.03 | 50.19 | ASD characteristics, cognition, family outcomes, adverse effects | Developmental | TAU | Home | Parent delivered | | 80 | Spjut Jansson 2016 [101] | Sweden | Cohort | 52 | 72.5 | 36.09 | ASD characteristics, adaptive functioning | Developmental | Eclectic | Home | Clinician | | 81 | Stadnick 2015 [102] | USA | Non-random | 30 | 80 | 54.83 | ASD characteristics, adaptive functioning, adverse effects | NDBI | TAU | Community | Parent delivered | | 82 | Stahmer 2020 [103] | USA | Non-random | 25 | 68 | 22.76 | ASD characteristics, adaptive functioning, cognition, family outcomes | NDBI | TAU | Community | Parent delivered |

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| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 83 | Strauss 2012 [104] | NA | Non-random | 44 | 93.18 | 49.43 | ASD characteristics, adaptive functioning, cognition, adverse effects | Behavioural | Eclectic | Home | Clinician and parent | | 84 | Sullivan 2014 [105] | USA | Random | 48 | 77.08 | 23.5 | ASD characteristics, cognition | NDBI | TAU | Home | Clinician and parent | | 85 | Tonge 2006 [106] | Australia | Random | 70 | 82.86 | 46.41 | Family outcomes, adverse effects | Behavioural | TAU | Health | Parent delivered | | 85 | Tonge 2014 [107] | Australia | Random | 70 | 82.86 | 46.67 | ASD characteristics, adaptive functioning, cognition | Behavioural | TAU | Health | Parent delivered | | 86 | Tsang 2007 [108] | Hong Kong | Non-random | 34 | 85.29 | 48.68 | ASD characteristics, adaptive functioning, cognition | TEACCH | TAU | Early education | Teacher delivered | | 87 | Van der Paelt 2016 [109] | Belgium | Cohort | 55 | 80 | 47.41 | ASD characteristics, adaptive functioning, cognition | Behavioural | Eclectic | Community | Clinician | | 87 | Van der Paelt 2016 [109] | Belgium | Cohort | 65 | 81.54 | 50.34 | ASD characteristics, adaptive functioning, cognition | Developmental | Eclectic | Community | Clinician | | 88 | Vernon 2019 [110] | USA | Random | 23 | 86.96 | 35.13 | ASD characteristics, adaptive functioning, cognition | NDBI | TAU | Community | Clinician and parent | | 88 | Barrett 2020 [111] | USA | Random | 21 | NR | 36.8 | ASD characteristics, cognition | NDBI | TAU | Home | Clinician and parent | | 89 | Vinen 2018 [112] | Australia | Cohort | 59 | 88.14 | 37.4 | ASD characteristics, cognition | NDBI | Eclectic | Community | Clinician |

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| Study | Article author, year | Country | Study design | N | Boys (%) | Age (mths) | Outcome domains reported | Intervention category | Control group | Primary setting | Person delivering | |—|—|—|—|—|—|—|—|—|—|—| | 90 | Vivanti 2014 [113] | Australia | Cohort | 57 | 87.72 | 41.18 | ASD characteristics, adaptive functioning, cognition | NDBI | Eclectic | Community | Clinician | | 91 | Warreyn 2014 [114] | Belgium | Random | 36 | 75 | 77.9 | ASD characteristics | Developmental | TAU | Health | Clinician | | 92 | Waters 2020 [115] | USA | Non-random | 94 | 95.74 | 40.1 | Adaptive functioning, cognition | Behavioural | TAU | Community | Clinician and parent | | 93 | Whalen 2010 [116] | USA | Random | 24 | NR | NR | Cognition | Technology-based | TAU | Early education | Teacher delivered | | 94 | Whitehouse 2017 [117] | Australia | Random | 75 | 78.75 | 39.78 | ASD characteristics, adaptive functioning, cognition | Technology-based | TAU | Home | Parent delivered | | 95 | Xu 2018 [118] | China | Random | 36 | 88.89 | 44.94 | ASD characteristics | NDBI | Eclectic | Early education | Teacher delivered | | 95 | Xu 2017 [119] | China | Random | 36 | 94.44 | 44.94 | ASD characteristics, cognition, adverse effects | NDBI | TAU | Early education | Teacher delivered | | 96 | Zachor 2007 [120] | NA | Cohort | 39 | 94.87 | 28.24 | ASD characteristics, cognition | Behavioural | Eclectic | Health | Clinician | | 97 | Zachor 2010 [121] | Israel | Cohort | 78 | 91.03 | 25.4 | Adaptive functioning, cognition | Behavioural | Eclectic | Early education | Clinician and parent | | 98 | Zhou 2018 [122] | China | Non-random | 43 | 88.37 | 26.55 | ASD characteristics, cognition, adverse effects | NDBI | TAU | Health | Parent delivered |

Note: ASD = autism spectrum disorder; NDBI = Naturalistic Developmental Behavioural Intervention; TAU = treatment as usual; NR = not reported.

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B3. Quality of the evidence used within this report

B3.1 Randomised controlled trials

The assessed risk of bias level (low risk, some concerns, high risk) for each of the five domains within the Cochrane RoB 2.0 tool [4] for each of the randomised controlled trial studies included within this report are displayed in Table B2.

Table B2: Domain and overall risk of bias assessments for included randomised controlled trials using Cochrane RoB 2.0

Notes: Study refers to the IDs in Table B1. Low risk assessments are highlighted in green. Risk assessments of some concern are highlighted in yellow. High risk assessments are highlighted in red.

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B3.2 Non-randomised study designs

The assessed risk of bias level (low risk, moderate risk, serious risk) for each of the five domains within the ROBINS-I tool [5] for each of the non-randomised studies included within this report are displayed in Table B4.

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Table B3: Domain and overall risk of bias assessments for included non-randomised studies using ROBINS-I

Notes: Study refers to the IDs in Table B1. Low risk assessments are highlighted in green. Moderate risk assessments are highlighted in yellow. Serious risk assessments are highlighted in red.

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B4. Efficacy across all and within individual outcome domains

B4.1 All outcome measures

The analysis of all outcome measures included 98 studies. The combined effect size was small and significant (g = 0.32, 95% CI 0.26 to 0.38, tau² = 0.11; Figure B2). The funnel plot did indicate evidence of small study effect (Figure B3), which was confirmed through formal testing (Egger’s intercept = 1.22, p = 0.002). Adjusting for this effect (imputing 19 studies) resulted in a reduction in effect size (Hedges’ g = 0.23, 95%CI 0.17 – 0.29, p < 0.001), although still small and statistically significant.

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Figure B2.1. Forest plot of all outcome measures

Note: Figures B2.1-B2.3 comprise one figure, displayed across multiple pages to ensure readability. An accessible version of the data displayed in this figure is presented in Table B4 below.

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Figure B2.2. Forest plot of all outcomes

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Figure B2.3. Forest plot of all outcomes

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Table B4. Table version of forest plot of all outcome measures.

Note:

This table presents the information displayed in Figure B2 in an accessible format. Positive Hedges’ g values favour the behaviourally based intervention, negative Hedges’ g values favour the control group.

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Figure B3. Funnel plot of ASD characteristic outcomes

B4.2 ASD characteristics

ASD characteristics outcomes were reported in 82 studies. The combined effect size was small and significant (g = 0.32, 95% CI 0.24 to 0.39, tau² = 0.11; Figure B4). The funnel plot did indicate evidence of small study effect (Figure B5), which was confirmed through formal testing (Egger’s intercept = 1.22, p = 0.002). Adjusting for this effect (imputing 14 studies) resulted in a reduction in effect size (Hedges’ g = 0.22, 95%CI 0.15 – 0.29, p < 0.001), although still small and statistically significant.

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Figure B4.1. Forest plot of ASD characteristic outcomes.

Note: Figures B4.1-B4.3 comprise one figure, displayed across multiple pages to ensure readability. An accessible version of the data displayed in this figure is presented in Table B5 below.

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Figure B4.2: Forest plot of ASD characteristic outcomes

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Figure B4.3: Forest plot of ASD characteristic outcomes.

Overall Effect (RVE) Prediction Interval

Table B5. Table version of forest plot of ASD characteristic outcome measures.

Note: This table presents the information displayed in Figure B4 in an accessible format. Positive Hedges’ g values favour the behaviourally based intervention, negative Hedges’ g values favour the control group.

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Figure B5. Funnel plot of ASD characteristic outcomes

B4.3 Adaptive functioning

Adaptive functioning outcomes were reported by 47 studies. The combined effect size was small and significant (g = 0.24, 95% CI 0.12 to 0.36, tau² = 0.09; Figure B6). The funnel plot did not indicate evidence of small study effect (Figure B7), which was confirmed through formal testing (Egger’s intercept = 0.73, p = 0.255).

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Figure B6.1. Forest plot of adaptive functioning outcomes

Note: Figures B6.1-B6.2 comprise one figure, displayed across multiple pages to ensure readability. An accessible version of the data displayed in this figure is presented in Table B6 below.

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Figure B6.2. Forest plot of adaptive functioning outcomes

Table B6. Table version of forest plot of adaptive functioning outcome measures.

Note: This table presents the information displayed in Figure B6 in an accessible format. Positive Hedges’ g values favour the behaviourally based intervention, negative Hedges’ g values favour the control group.

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Overall Effect (RVE): 0.24 (0.12, 0.36) Prediction Interval: (-0.38, 0.86) NA

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Figure B7. Funnel plot of adaptive functioning outcomes

B4.4 Cognition and language

Cognition and language outcomes were reported by 64 studies. The combined effect size was small and significant (g = 0.30, 95% CI 0.22 to 0.38, tau2 = 0.05; Figure B8). The funnel plot did indicate evidence of small study effect (Figure B9), which was confirmed through formal testing (Egger’s intercept = 1.63, p < 0.001). Adjusting for this effect (imputing 15 studies) resulting in a reduction in effect size (Hedges’ g = 0.19, 95%CI 0.10 – 0.28, p < 0.001), although still small and statistically significant.

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Figure B8.1. Forest plot of cognition outcomes

Note: Figures B8.1-B8.2 comprise one figure, displayed across multiple pages to ensure readability. An accessible version of the data displayed in this figure is presented in Table B7 below.

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Figure B8.2. Forest plot of cognition outcomes

[Image of forest plot]

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Table B7. Table version of forest plot of cognition and language outcome measures.

Note:

This table presents the information displayed in Figure B8 in an accessible format. Positive Hedges’ g values favour the behaviourally based intervention, negative Hedges’ g values favour the control group.

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Figure B9. Funnel plot of cognition and language outcomes

B4.5 Family outcomes

Family outcomes were reported by 20 studies. The combined effect size was small and significant (g = 0.39, 95% CI 0.21 to 0.58, tau2 = 0.18; Figure B10). The funnel plot did not indicate evidence of small study effect (Figure B11), which was confirmed through formal testing (Egger’s intercept = 0.48, p = 0.533).

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Figure B10. Forest plot of family outcomes

Note:

An accessible version of the data displayed in this figure is presented in Table B8 below.

Table B8. Table version of forest plot of family outcome outcome measures.

Note:

This table presents the information displayed in Figure B10 in an accessible format. Positive Hedges’ g values favour the behaviourally based intervention, negative Hedges’ g values favour the control group.

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Figure B11. Funnel plot of family outcomes

B4.6 Adverse effects

Adverse effects outcomes were reported by 27 studies. The combined effect size was small and significant (g = 0.24, 95% CI 0.09 to 0.39, tau² = 0.09; Figure B12). The funnel plot did not indicate evidence of small study effect (Figure B13), which was confirmed through formal testing (Egger’s intercept = 0.96, p = 0.220).

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Figure B12. Forest plot of adverse effects outcomes

Note: An accessible version of the data displayed in this figure is presented in Table B9, which follows.

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Table B9. Table version of forest plot of adverse effects outcome measures.

Note: This table presents the information displayed in Figure B12 in an accessible format. Positive Hedges’ g values favour the behaviourally based intervention, negative Hedges’ g values favour the control group.

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Figure B13. Funnel plot of adverse effects outcomes

B5. Investigating the effect of dose

B5.1 Relationship between dose and efficacy

Table B10 provides model statistics for the linear models which investigate the relationship between dose (monthly and total clinician hours) and efficacy of behaviourally based interventions as compared to a control group. This is reported across all available outcomes, as well as within three outcome domains: ASD characteristics, adaptive functioning, and cognition and language.

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Table B10.1. Linear model statistics for association between dose (monthly clinician hours) and ASD characteristic, adaptive functioning and cognition and language outcomes.

Note: These tables have been grouped together using a number-letter referencing system as they are related.

Table B10.2. Linear model statistics for association between dose (total clinician hours) and ASD characteristic, adaptive functioning and cognition and language outcomes.

Relationship between dose and change from baseline to follow-up separately within the intervention group and the comparison group

Table B11 provides model statistics for the linear models which investigate the relationship between dose (monthly and total clinician hours) and change from pre- to post-intervention in the group of children who underwent behaviourally based intervention. This is reported across all available outcomes, as well as within three outcome domains: ASD characteristics, adaptive functioning, and cognition and language.

Table B11.1. Linear model statistics for association between dose (monthly clinician hours) and ASD characteristic, adaptive functioning and cognition and language outcomes for change from pre to post in the intervention group.

Note: These tables have been grouped together using a number-letter referencing system as they are related.

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Table B11.2. Linear Model Statistics for Association Between Dose (Total Clinician Hours) and ASD Characteristics, Adaptive Functioning, and Cognition and Language Outcomes for Change from Pre to Post in the Intervention Group

All outcomes

The linear and non-linear models of total and monthly clinician-delivered hours of intervention by effect size for the change from baseline to follow-up in the behaviourally based intervention group, with 95% confidence intervals, for all outcomes are shown in Figure B14.

Figure B14.1. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in all outcomes from pre- to post-intervention in the intervention group.

Note: These figures have been grouped together using a number-letter referencing system as they are related. Hedges’ g > 0 = improvement in outcomes from baseline to follow-up. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up.

Commented [GE45]: Alt text: Displays the linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g for the intervention group and the comparison group. Hedges’ g from 0.25 at 0 hours to 1.0 at 150 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Figure B14.2. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for change in all outcomes from pre- to post-intervention in the intervention group.

[Image not converted to Markdown – “Figure B14.2” – check the source PDF page for the actual content]

Commented [GE46]: Alt text: Displays the linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for the intervention group. Hedges’ g value increases from 0.45 at 0 hours to 0.6 at 5000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

Figure B14.3. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in all outcomes from pre- to post-intervention in the intervention group.

[Image not converted to Markdown – “Figure B14.3” – check the source PDF page for the actual content]

Commented [GE47]: Alt text: Displays the non-linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for the intervention group . Hedges’ g value increases from 0.2 at 0 hours to 0.75 at 75 hours, then decreases to 0.5 at 150 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Figure B14.4. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in all outcomes from pre- to post-intervention in the intervention group.

Commented [GE48]: Alt text: Displays the non-linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for the intervention group. Hedges’ g value increases slightly from 0.5 at 0 hours to 0.6 at 5000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

The linear and non-linear models of total and monthly clinician-delivered hours of intervention by effect size for the change from baseline to follow-up in the behaviourally based intervention group, with 95% confidence intervals, for ASD characteristic outcomes are shown in Figure B15.

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Figure B15.1. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for

change in ASD characteristic outcomes from pre- to post-intervention in the intervention group.

Note: These figures have been grouped together using a number-letter referencing system as they are related. Hedges’ g > 0 = improvement in outcomes from baseline to follow-up. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up.


Figure B15.2. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in ASD characteristic outcomes from pre- to post-intervention in the intervention group.


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Figure B15.3. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in ASD characteristic outcomes from pre- to post-intervention in the intervention group.


Displays the non-linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g for the intervention group. Hedges’ g value increases from 0.1 at 0 hours to 0.5 at 20 hours, remains stable until 50 hours, then increases to 0.75 at 150 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

Figure B15.4. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for change in ASD characteristic outcomes from pre- to post-intervention in the intervention group.


Displays the non-linear relationship between total clinician-delivered hours (from 0 to 5000 hours) and Hedges’ g for the intervention group. Hedges’ g value for the intervention group increases from 0 at 0 hours to 0.75 at 100 hours, then gradually decreases to 0.5 at 3000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Adaptive functioning

The linear and non-linear models of total and monthly clinician-delivered hours of intervention by effect size for the change from baseline to follow-up in the behaviourally based intervention group, with 95% confidence intervals, for adaptive functioning outcomes are shown in Figure B16.

Figure B16. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in adaptive functioning outcomes from pre- to post-intervention in the intervention group.

Note: These figures have been grouped together using a number-letter referencing system as they are related. Hedges’ g > 0 = improvement in outcomes from baseline to follow-up. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up.

![Displays the linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g for the intervention group. Hedges’ g for the intervention group increases from 0 at 0 hours to 1.0 at 150 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.]

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Figure B16.2. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in adaptive functioning outcomes from pre- to post-intervention in the intervention group.

![Alt text: Displays the linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for the intervention group. Hedges’ g for the intervention group increased slightly from 0.3 at 0 hours to 0.4 at 150 hours. Individual points are scattered greatly around the line of best fit.]

Values mentioned here are approximate.

Figure B16.3. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g)

for change in adaptive functioning outcomes from pre- to post-intervention in the invention group.

![Alt text: Displays the non-linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for the intervention group. Hedges’ g value for the intervention group increases from -0.1 at 0 hours to 1.0 at 100 hours, then decreases to 0.1 at 150 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.]

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Figure B16.4. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in adaptive functioning outcomes from pre- to post-intervention in the intervention group.

(a)(ii) - irrelevant)

Displays the non-linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for the intervention group. Hedges’ g value for the intervention group increases from -0.5 at 0 hours to 1.1 at 1000 hours, then decreases to 0.8 at 3000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

The linear and non-linear models of total and monthly clinician-delivered hours of intervention by effect size for the change from baseline to follow-up in the behaviourally based intervention group, with 95% confidence intervals, for cognition and language outcomes are shown in Figure B17.

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Figure B17.1. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in cognition and language outcomes from pre- to post-intervention in the intervention group.

Note: These figures have been grouped together using a number-letter referencing system as they are related. Hedges’ g > 0 = improvement in outcomes from baseline to follow-up. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up.


Displays the linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g for the intervention group. Hedges’ g value for the intervention group increases from 0.5 at 0 hours to 1.2 at 150 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

Figure B17.2. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for change in cognition and language outcomes from pre- to post-intervention in the intervention group.


Displays the linear relationship between total clinician-delivered hours (from 0 to 5000 hours) and Hedges’ g for the intervention group. Hedges’ g value for the intervention group increases from 0.6 at 0 hours to 1.0 at 3000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Figure B17.3. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g)

for change in cognition and language outcomes from pre- to post-intervention in the intervention group.

Commented [GE59]: Alt text: Displays the non-linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for the intervention group. Hedges’ g value for the intervention group decreases from 0.6 at 0 hours to 0.4 at 30 hours, then increases to 1.2 at 100 hours and decreases to 0.8 at 150 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

Figure B17.4. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in cognition and language outcomes from pre- to post-intervention in the intervention group.

Commented [GE60]: Alt text: Displays the non-linear relationship between total clinician- delivered hours (from 0 to 5000 hours) and Hedges’ g for the intervention group. Hedges’ g value for the intervention group increases from 0.5 at 0 hours to 1.1 at 3000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Official

Comparison group

Table B12 provides model statistics for the linear models which investigate the relationship between dose (monthly and total clinician hours) and change from baseline to follow-up in the group of children who did not undergo behaviourally based intervention (i.e., the comparison group). This is reported across all available outcomes, as well as within three outcome domains: ASD characteristics, adaptive functioning, and cognition and language.

Table B12.1. Linear model statistics for association between dose (monthly clinician hours) and ASD characteristic, adaptive functioning and cognition and language outcomes for change from baseline to follow-up in the comparison group

Note: These tables have been grouped together using a number-letter referencing system as they are related.

Table B12.2. Linear model statistics for association between dose (total clinician hours) and ASD characteristic, adaptive functioning and cognition and language outcomes for change from baseline to follow-up in the comparison group

All outcomes

The linear and non-linear models of total and monthly clinician-delivered hours of intervention by effect size for the change from baseline to follow-up in the comparison group, with 95% confidence intervals, for all outcomes are shown in Figure B18.

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Figure B18.1. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in all outcomes from pre- to post-intervention in the comparison group.

Note: These figures have been grouped together using a number-letter referencing system as they are related. Hedges’ g > 0 = improvement in outcomes from baseline to follow-up. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up.


Commented [GE61]: Alt text: Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for the comparison group. Hedges’ g value remains constant at 0.25 across hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

Figure B18.2. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for change in all outcomes from pre- to post-intervention in the comparison group.


Commented [GE62]: Alt text: Displays the linear relationship between total clinician- delivered hours (from 0 to 2000 hours) and Hedges’ g for the comparison group. Hedges’ g value increases slightly from 0.2 at 0 hours to 0.4 at 2000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Figure B18.3. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in all outcomes from pre- to post-intervention in the comparison group.


Commented [GE63]: Alt text: Displays the non-linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for the comparison group. Hedges’ g value increases from 0 at 15 hours to 0.4 at 50 hours, then decreases to 0.2 at 100 hours, where it remains relatively stable. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

Figure B18.4. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in all outcomes from pre- to post-intervention in the comparison group.


Commented [GE64]: Alt text: Displays the non-linear relationship between total clinician- delivered hours (from 0 to 2000 hours) and Hedges’ g for the comparison group. Hedges’ g value increases from 0.1 at 0 hours to 0.3 at 400 hours, then decreases to 0 at 100 hours, then increases to 0.5 at 2000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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ASD characteristics

The linear and non-linear models of total and monthly clinician-delivered hours of intervention by effect size for the change from baseline to follow-up in the comparison group, with 95% confidence intervals, for ASD characteristic outcomes are shown in Figure B19.

Figure B19.1. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in ASD characteristic outcomes from pre- to post-intervention in the comparison group.

Note: These figures have been grouped together using a number-letter referencing system as they are related. Hedges’ g > 0 = improvement in outcomes from baseline to follow-up. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up.

Commented [GE65]: Alt text: Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for the comparison group. Hedges’ g value remains constant at 0.25 across hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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Figure B19.2. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in ASD characteristic outcomes from pre- to post-intervention in the comparison group.

![Alt text: Displays the linear relationship between total clinician- delivered hours (from 0 to 2000 hours) and Hedges’ g for the comparison group. Hedges’ g value increases slightly from 0.2 at 0 hours to 0.4 at 2000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.]

Figure B19.3. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g)

for change in ASD characteristic outcomes from pre- to post-intervention in the comparison group.

![Alt text: Displays the non-linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for the comparison group. Hedges’ g value increases from 0 at 20 hours to 0.4 at 50 hours, then decreases to 0.1 at 150 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.]

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Figure B19.4. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in ASD characteristic outcomes from pre- to post-intervention in the comparison group.

Commented [GE68]: Alt text: Displays the non-linear relationship between total clinician- delivered hours (from 0 to 2000 hours) and Hedges’ g for the comparison group. Hedges’ g value increases from 0 at 0 hours to 0.3 at 200 hours, then decreases to 0.1 at 800 hours, then increases to 0.5 at 2000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

The linear and non-linear models of total and monthly clinician-delivered hours of intervention by effect size for the change from baseline to follow-up in the comparison group, with 95% confidence intervals, for adaptive functioning outcomes are shown in Figure B20.

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Figure B20.1. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for

change in adaptive functioning outcomes from pre- to post-intervention in the comparison group.

Note: These figures have been grouped together using a number-letter referencing system as they are related. Hedges’ g > 0 = improvement in outcomes from baseline to follow-up. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up.

[Image not converted to Markdown – “Figure B20.1” – check the source PDF page for the actual content]

                                                                                             Commented [GE69]: Alt text:

Displays the linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for the comparison group. Hedges’ g value decreases from 0.5 at 25 hours to 0.2 at 130 hours. The 95% confidence interval is very large. Values mentioned here are approximate.

Figure B20.2. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in adaptive functioning outcomes from pre- to post-intervention in the comparison group.

[Image not converted to Markdown – “Figure B20.2” – check the source PDF page for the actual content]

                                                                                             Commented [GE70]: Alt text:

Displays the linear relationship between total clinician- delivered hours (from 0 to 2000 hours) and Hedges’ g for the comparison group. Hedges’ g value increased from 0.1 at 0 hours to 0.8 at 2000 hours. The 95% confidence interval is very wide. Values mentioned here are approximate.

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Figure B20.3. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in adaptive functioning outcomes from pre- to post-intervention in the comparison group.


Displays the non-linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g for the comparison group. Hedges’ g value increases from 0 at 20 hours to 0.5 at 60 hours, then decreases to 0 at 125 hours. The 95% confidence interval is very wide. Values mentioned here are approximate.

Figure B20.4. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for change in adaptive functioning outcomes from pre- to post-intervention in the comparison group.


Displays the non-linear relationship between total clinician-delivered hours (from 0 to 2000 hours) and Hedges’ g for the comparison group. Hedges’ g value for the comparison group remains stable at 0.2 from 0 to 500 hours, then decreases to -0.2 at 1250 hours, then increases to 1.1 at 2000 hours. The 95% confidence interval is very wide. Values mentioned here are approximate.

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Cognition and language

The linear and non-linear models of total and monthly clinician-delivered hours of intervention by effect size for the change from baseline to follow-up in the comparison group, with 95% confidence in tervals, for cognition and language outcomes are shown in Figure B21.

Figure B21.1. Linear model of monthly clinician-delivered dose by effect size (Hedges’ g) for change in cognition and language outcomes from pre- to post-intervention in the comparison group.

Note: These figures have been grouped together using a number-letter referencing system as they are related. Hedges’ g > 0 = improvement in outcomes from baseline to follow-up. Hedges’ g > 0 = decrease in outcomes from baseline to follow-up.

![Displays the linear relationship between monthly clinician-delivered hours (from 0 to 150 hours) and Hedges’ g for the comparison group. Hedges’ g value for the comparison group increases from 0.2 at 20 hours to 0.5 at 150 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.]

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Figure B21.2. Linear model of total clinician-delivered dose by effect size (Hedges’ g) for

change in cognition and language outcomes from pre- to post-intervention in the comparison group.

![Alt text: Displays the linear relationship between total clinician- delivered hours (from 0 to 2000 hours) and Hedges’ g for the comparison group. Hedges’ g value increases from 0.2 at 100 hours to 0.5 at 2000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.]

Figure B21.3. Non-linear model of monthly clinician-delivered dose by effect size (Hedges’ g)

for change in cognition and language outcomes from pre- to post-intervention in the comparison group.

![Alt text: Displays the non-linear relationship between monthly clinician- delivered hours (from 0 to 150 hours) and Hedges’ g for the comparison group. Hedges’ g value is around 0.25 from 20 hours to 60 hours, where it then remains stable at 0.5 until 150 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.]

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Figure B21.4. Non-linear model of total clinician-delivered dose by effect size (Hedges’ g) for change in cognition and language outcomes from pre- to post-intervention in the comparison group.

[Image not converted to Markdown – “Figure B21.4” – check the source PDF page for the actual content]

Commented [GE76]: Alt text: Displays the non-linear relationship between total clinician- delivered hours (from 0 to 2000 hours) and Hedges’ g for the comparison group. Hedges’ g value decreases from 0.25 at 0 hours to 0.15 at 1000 hours, then increases to 0.5 at 2000 hours. Individual points are scattered greatly around the line of best fit. Values mentioned here are approximate.

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B6. Investigigating the effect of population, intervention, and study design factors on efficacy

Results and forest plots for each subgroup analysis are shown for each outcome domain in Figures B22-B26.

Figure B22. Results of subgroup analysis for ASD characteristic outcome domain

Note: An accessible version of the data displayed in this figure is presented in Table B13, which follows. The F and the p statistic are from the Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. Tau² is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

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Table B13. Table version of results of subgroup analysis for ASD characteristic outcome domain

Note: This table presents the information displayed in Figure B22 in an accessible format. The F and the p statistic are from the Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. Tau² is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

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Figure B23. Results of subgroup analysis for adaptive functioning outcome domain

Note: An accessible version of the data displayed in this figure is presented in Table B14, which follows. The F and the p statistic are from the Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. Tau² is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

Table B14. Table version of results of subgroup analysis for adaptive functioning outcome domain

Note: This table presents the information displayed in Figure B23 in an accessible format. The F and the p statistic are from the Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. Tau² is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

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Figure B24. Results of subgroup analysis for cognition and language outcome domain

Note: An accessible version of the data displayed in this figure is presented in Table B15, which follows. The F and the p statistic are from the Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. Tau² is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

Table B15. Table version of results of subgroup analysis for cognition and language outcome domain

Note: This table presents the information displayed in Figure B24 in an accessible format. The F and the p statistic are from the Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. Tau² is a

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measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

Figure B25. Results of subgroup analysis for family outcomes domain

Note: An accessible version of the data displayed in this figure is presented in Table B16, which follows. The F and the p statistic are from the Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. Tau² is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

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Table B16. Table version of results of subgroup analysis for family outcome domain

Note: This table presents the information displayed in Figure B25 in an accessible format. The F and the p statistic are from the Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. Tau² is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

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Figure B26. Results of subgroup analysis for adverse effects outcome domain

Note: An accessible version of the data displayed in this figure is presented in Table B17, which follows. The F and the p statistic are from the Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. Tau² is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

Table B17. Table version of results of subgroup analysis for adverse effects outcome domain

Note: This table presents the information displayed in Figure B26 in an accessible format. The F and the p statistic are from the Wald-type test. If statistically significant (p < 0.05), this indicates that there is a difference in efficacy of the intervention between levels of the subgroup. Tau² is a measure of statistical heterogeneity, which gives an estimation of the extent to which an effect estimate is inconsistent across studies.

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