Getting SMART about Adaptive Interventions: Applications in...
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Getting SMART about Adaptive Interventions:Applications in Autism and Implementation Science
Daniel Almirall and FriendsSurvey Research Center
Institute for Social ResearchUniversity of Michigan
24 February 2019
University of MinnesotaInstitute for Translational Research
In Children’s Mental Health
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CollaboratorsMethods/Statistics Collaborators
Inbal Nahum-Shani, Mich
Susan A. Murphy, Harvard
Linda M. Collins, Penn State
Daniel F. McCaffrey, ETS
Behavioral Science Collaborators
Connie Kasari, UCLA
Amy Kilbourne, Mich
Megan Patrick, UMN/ITR
Meredith G-S, UMN/ITR
Current Statistics Students
Nick Seewald, PhD
Brook Luers, PhD
Tim NeCamp, PhD
Olivia Hackworth, PhD
Madison Stoms, Ugrad
Sophia Luo, Ugrad
Previous Statistics Students
Xi Lu, Penn State, Google
Robert Yuen, Liberty Mutual
Kelly Hall, Princeton
Josh Kim, Applied Grad School
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Outline (Don’t worry, I don’t really have 100+ slides!)
Adaptive Interventions
Multi-phase Optimization STrategy (MOST) Framework
Sequential Multiple Assignment Randomized Trials (SMART)
Case Studies
I SMART: Minimally-verbal Children with AutismI Pilot SMART: Social Engagement in Autism-inclusive SchoolsI Clustered SMART: Adaptive Implementation of CBT in Schools
Seemingly-Restricted SMART Designs
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Outline (Don’t worry, I don’t really have 100+ slides!)
Adaptive Interventions
Multi-phase Optimization STrategy (MOST) Framework
Sequential Multiple Assignment Randomized Trials (SMART)
Case Studies
I SMART: Minimally-verbal Children with AutismI Pilot SMART: Social Engagement in Autism-inclusive SchoolsI Clustered SMART: Adaptive Implementation of CBT in Schools
Seemingly-Restricted SMART Designs
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Intervention often entails a sequential, individualized approachwhereby treatment is adapted and re-adapted over time...
...Adaptive Interventions provide a guide for intervening this way
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Adaptive Intervention (Dynamic Treatment Regimen)
In words
A pre-specified sequence of decision-rules used to guide whether, how, orwhen—and based on which measures—to alter an intervention (e.g.,
monitoring schedule, treatment type, duration, frequency or amount) atcritical decision points during the course of education/care.
In symbols
Given knowledge about a student’s changing status over time{S1 = s1, a1, S2(a1) = s2, a2, . . . ,ST (aT−1) = sT , aT}
a T -stage adaptive intervention is{d1(s1)→ a1, d2(s1, a1, s2)→ a2, . . . , dT (aT−1, sT )→ aT}.
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Adaptive Intervention (Dynamic Treatment Regimen)
In words
A pre-specified sequence of decision-rules used to guide whether, how, orwhen—and based on which measures—to alter an intervention (e.g.,
monitoring schedule, treatment type, duration, frequency or amount) atcritical decision points during the course of education/care.
In symbols
Given knowledge about a student’s changing status over time{S1 = s1, a1, S2(a1) = s2, a2, . . . ,ST (aT−1) = sT , aT}
a T -stage adaptive intervention is{d1(s1)→ a1, d2(s1, a1, s2)→ a2, . . . , dT (aT−1, sT )→ aT}.
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An Example Adaptive Intervention
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Example of an Adaptive Intervention in AutismSome Background First...
≥50% of children with autism who received interventions beginningat age 2 remained non-verbal at age 9
Failure to develop spoken language by age 5 = poor prognosis
Evidence Base:I Discrete Trials Training,I Joint Attention, Symbolic Play, Engagement & Regulation (JASPER)I Enhanced Milieu Teaching (EMT)
Promising: Augmentative, Alternative Communication (AAC) devices
But AAC’s are costly & not all children need it.I Research is limited. Mostly single-subject studies. No rigorous trials.
Motivation for an adaptive intervention involving AAC’s in contextof JASPER-EMT among older, minimally-verbal children with
autism.
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Example of an Adaptive Intervention in AutismSome Background First...
≥50% of children with autism who received interventions beginningat age 2 remained non-verbal at age 9
Failure to develop spoken language by age 5 = poor prognosis
Evidence Base:I Discrete Trials Training,I Joint Attention, Symbolic Play, Engagement & Regulation (JASPER)I Enhanced Milieu Teaching (EMT)
Promising: Augmentative, Alternative Communication (AAC) devices
But AAC’s are costly & not all children need it.
I Research is limited. Mostly single-subject studies. No rigorous trials.
Motivation for an adaptive intervention involving AAC’s in contextof JASPER-EMT among older, minimally-verbal children with
autism.
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Example of an Adaptive Intervention in AutismSome Background First...
≥50% of children with autism who received interventions beginningat age 2 remained non-verbal at age 9
Failure to develop spoken language by age 5 = poor prognosis
Evidence Base:I Discrete Trials Training,I Joint Attention, Symbolic Play, Engagement & Regulation (JASPER)I Enhanced Milieu Teaching (EMT)
Promising: Augmentative, Alternative Communication (AAC) devices
But AAC’s are costly & not all children need it.I Research is limited. Mostly single-subject studies. No rigorous trials.
Motivation for an adaptive intervention involving AAC’s in contextof JASPER-EMT among older, minimally-verbal children with
autism.
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Example of an Adaptive Intervention in AutismFor minimally verbal children with autism spectrum disorder
Stage One JASP+EMT for 12 weeks;Stage Two At the end of week 12, determine early sign of response:
I IF slow responder: Augment JASP+EMT with AAC for 12 weeks;I ELSE IF responder: Maintain JASP+EMT for 12 weeks.
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Example of an Adaptive Intervention in AutismFor minimally verbal children with autism spectrum disorder
Stage One JASP+EMT for 12 weeks;Stage Two At the end of week 12, determine early sign of response:
I IF slow responder: Augment JASP+EMT with AAC for 12 weeks;I ELSE IF responder: Maintain JASP+EMT for 12 weeks.
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How was response/slow-response defined?
Percent change from baseline to week 12 was calculated for 7variables:
7 variables: socially communicative utterances (SCU), percent SCU,mean length utterance, total word roots, words per minute, totalcomments, unique word combinations
Fast Responder: if ≥25% change on 7 measures;
Slower Responder: otherwise (this includes kids with no improvement,which is rare)
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Three Directions for Your Adaptive Interventions Research
1. Need to prepare or address feasibility/acceptability?
2. Learn how best to build/optimize an adaptive intervention?
3. Ready to evaluate an adaptive intervention?
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Multi-phase Optimization STrategy (MOST) FrameworkCollins (2018); Collins & Kugler (2018); Almirall, et al. (2018)
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Three Directions for Your Adaptive Interventions Research
1. Prepare? Feasibility/acceptability?⇒ e.g., pilot study, observational study, pilot SMART
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Three Directions for Your Adaptive Interventions Research
2. Build/optimize?⇒ e.g., Enhanced non-responder trial, SMARTs, MRT
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Three Directions for Your Adaptive Interventions Research
3. Evaluate?e.g., standard RCT, non-inferiority trial
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Example of a Confirmatory RCT of an AdaptiveIntervention
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MOST Framework (Collins et al 2011, Ann Beh Med)
e.g., Pilot SMART,Obs Studies
e.g., SMART
e.g., Standard RCTnot our focus
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Typical Scientific Questions when Building/Optimizing anAdaptive Intervention.
Is it better to provide AAC from the start?
Who benefits from initial AAC versus who benefits from delayed AAC?
For slow responders, what is the effect of providing the AAC vsintensifying JASP (not providing AAC)?
Why begin treatment with JASP? Why not a high-quality course ofcore-principles of discrete trials training?
Should we train parents following an initial successful course oftreatment? If so, which families benefit most from this?
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Insufficient Empirical Evidence or Theories to Address suchQuestions
In the past we have relied on
Expert opinion
Clinical expertise
Piecing together an adaptive intevrention using results from separateRCTs
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Sequential Multiple Assignment Randomized Trials (SMART)
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What is a Sequential Multiple Assignment RandomizedTrial (SMART)?
A type of multi-stage, randomized trial design—a factorial design.
At each stage, subjects randomized to a set of feasible/ethicaltreatment options.
Treatment options at latter stages may be restricted by response toearlier treatments.
SMARTs were developed explicitly for the purpose of building ahigh-quality Adaptive Intervention.
SMARTs are not (typically) used for confirming the effectiveness ofan AI (evaluation).
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What is a Sequential Multiple Assignment RandomizedTrial (SMART)?
A type of multi-stage, randomized trial design—a factorial design.
At each stage, subjects randomized to a set of feasible/ethicaltreatment options.
Treatment options at latter stages may be restricted by response toearlier treatments.
SMARTs were developed explicitly for the purpose of building ahigh-quality Adaptive Intervention.
SMARTs are not (typically) used for confirming the effectiveness ofan AI (evaluation).
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Prototypical SMART
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SMART Case Study:
Characterizing Cognition in Non-verbal Individuals with ASD (CCNIA)
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Example of a (first-ever) SMART in Autism ResearchPI: Kasari (UCLA)
The population of interest:
Children with autism spectrum disorder
Age: 5-8
Minimally verbal: <20 spontaneous words in a 20-min. language test
History of treatment: ≥2 years of prior intervention
Functioning: ≥2 year-old on non-verbal intelligence tests
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Example of a SMART in Autism Research (N = 61)PI: Kasari (UCLA)
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SMARTs permit scientists to answer many interestingquestions for building a high-quality adaptive intervention.
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A Common Primary Aim in a SMART is theComparison of “Embedded” Adaptive Interventions
Over the past 3 years, a good portion of my time has been devotedto developing methods to do this with a repeated-measuresoutcome arisinh from a SMART.
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Three AIs “Embedded” in this Example Autism SMART
(JASP,JASP+)
(JASP,AAC)
(AAC,AAC+)
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Three AIs “Embedded” in this Example Autism SMART
(JASP,JASP+)
(JASP,AAC)
(AAC,AAC+)
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A Common Primary Aim in a SMART is theComparison of these Embedded Adaptive Interventions
Methodological challenges include
Modeling Considerations: The intermixing of repeated measures andsequential randomizations required new modeling considerations
Effect Interpretation: Traditional comparison of slopes may no longerbe appropriate; need for a clinically relevant, easy-to-understandsummary measure of the mean trajectories
Statistical Efficiency: Permit domain scientists to take advantage ofthe within-person correlation in the outcome in a way they areaccustomed to
Random Effects: Psuedo-likelihood with a rather non-standard form
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Example of a SMART in Autism Research (N = 61)PI: Kasari (UCLA)
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Longitudinal Outcomes in this Example SMART in Autism
Outcomes collected at baseline, and weeks 12, 24 and 36
Primary outcome (verbal): from 20-minute “natural language sample”:
Total spontaneous communicative utterances (TSCU)
Secondary outcome (non-linguistic):
Initiating joint attention (IJA; e.g., pointing; JASP mechanism)
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Results
Adaptive (a) TSCU (b) IJAIntervention AUC 95% CI AUC 95%CI
(AAC,AAC+) 51.7 [43, 60] 9.5 [7.2,11.8](JASP,AAC) 36.0 [28, 44] 7.2 [5.6,8.8]
(JASP,JASP+) 33.1 [25, 42] 6.6 [5,8.2]No diff null p < 0.01 p < 0.05
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You may be wondering about sample size/power?
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Prototypical SMART
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Sample Size Formulae with Repeated-measures AnalysesFor comparing 2 embedded AIs that begin with different treatments in a prototypicalSMART; Hypothesis test is that there is no difference in means at the end of the study
N ≥ 4(z1−α/2+z1−β)2
δ2 × (2− r)×(1− ρ2
)where
r = response rate after stage 1 treatment
ρ = within-person correlation in outcome
r = ρ = δ = 1/2, α = .05, need N = 142 for 80% power.Same question using a standard 2-arm trial requires N = 96.
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Sample Size Formulae with Repeated-measures AnalysesFor comparing 2 embedded AIs that begin with different treatments in a prototypicalSMART; Hypothesis test is that there is no difference in means at the end of the study
N ≥ 4(z1−α/2+z1−β)2
δ2 × (2− r)×(1− ρ2
)where
r = response rate after stage 1 treatment
ρ = within-person correlation in outcome
r = ρ = δ = 1/2, α = .05, need N = 142 for 80% power.Same question using a standard 2-arm trial requires N = 96.
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References
Lu, Nahum-Shani, Kasari, Lynch, Oslin, Pelham, Fabiano, Almirall. (2016).Comparing DTRs using repeated-measures outcomes: modelingconsiderations in SMART studies, Statistics in Medicine.
Almirall, DiStefano, Chang, Shire, Lu, Nahum-Shani, Kasari, C. (2016).Adaptive interventions and longitudinal outcomes in minimally verbalchildren with ASD: Role of speech-generating devices, Journal of ClinicalChild and Adolescent Psychology.
Seewald, Nahum-Shani, McKay, Almirall (about to be submitted). Samplesize considerations for comparing DTRs in a sequentially-randomized trialwith a continuous longitudinal outcome. about to be submitted
Luers, Qian, Nahum-Shani, Almirall (in progress). LongitudinalMixed-effects Models to compare Dynamic Treatment Regimens inSequentially-randomized Trials to be submitted in April
NeCamp, Kilbourne, Almirall (2017). Cluster-level adaptive interventionsand sequential, multiple assignment, randomized trials: Estimation andsample size considerations, Statistical Methods in Medical Research.
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Two More SMART Case Studies
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SMART Case Study:
Getting SMART about Social and Academic Engagement in ElementaryAged Children with ASD
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Social & Academic Engagement in School-Children w/ASDBackground
Targeting students with ASD, ages 5-12, ≥ 70 IQ
In inclusive schools/classrooms in the LAUSD
Improve social skills, behavioral, and academic engagement outcomes
Early on, social skills interventions rarely studied in the child’s naturalenvironment (befuddles me!); so had no “generalization”
Others that were in the child’s natural environment, such as 1:1pairing with an aide at the school, turned out to have iatrogeniceffects!
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Social & Academic Engagement in School-Children w/ASDSet-up and Questions
Evidence Base: Remaking Recess (school-level), Classroom Supports(class-level), Parent-assisted social skills intervention (individual-levelat home), Peer-mediated social skills intervention (individual-level)
But no empirically derived guidelines to help guide interventiondecision making of children with ASD in schools.
Combined Peer + Parent is also promising, but this cannot beprovided to all children due to cost and not all children need it
Begin individual level interventions with Peer or with Parent?
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Academic & Social Engagement in School-Children w/ASDPIs: Kasari; Co-I: Almirall; IES-funded Pilot SMART
This is a 2-arm trial. But my mad scientist friend had some other scientificquestions, leading to a different design...
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Social & Academic Engagement in School-Children w/ASDSet-up and Questions Motivating the SMART
Evidence Base: Remaking Recess (school-level), ClassroomSupports (class-level), Parent-assisted social skills intervention(individual-level at home), Peer-mediated social skills intervention(individual-level)
But no empirically derived guidelines to help guide interventiondecision making of children with ASD in schools.
Begin individual level interventions with Peer or with Parent?
Do all classrooms require Classroom Supports?
Synergistic effect between CS and individual-level interv.?
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Academic & Social Engagement in School-Children w/ASDPIs: Kasari; Co-I: Almirall; IES-funded Pilot SMART
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Primary and Secondary Aims of this Pilot SMART
Primary Aim: Address feasibility and acceptability concerns related tothe embedded adaptive interventions
I identifying children as early vs. slower responders by theparaprofessionals in the context of RR,
I transitioning children to Parent or Peer at wk8,
I providing augmented Peer+Parent to slower responders
I not providing augmented treatment to early responders at wk20
I satisfaction with txt sequences by children, parents, teachers,paraprofessionals & school champions
I teacher-rated measures of progress during CS for deciding Parent vsPeer
Secondary Aim: To obtain preliminary data to support a full-scaleSMART.
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References
Almirall, Kasari, McCaffrey, Nahum-Shani. (2018). Developing OptimizedAdaptive Interventions in Education, Journal of Research on EducationalEffectiveness.
Almirall, Nahum-Shani, Wang, Kasari. (2018). Experimental Designs forResearch on Adaptive Interventions: Singly- and Sequentially-RandomizedTrials, Optimization of Multicomponent Behavioral Biobehavioral andBiomedical Interventions using MOST. L. Collins, K. Kugler (Editors).
Almirall, Compton, Gunlicks-Stoessel, Duan, Murphy (2012). Designing aPilot Sequential Multiple Assignment Randomized Trial for Developing anAdaptive Treatment Strategy.” Statistics in Medicine
Kim, H. and Almirall (2016). A sample size calculator for SMART pilotstudies, SIAM Undergraduate Research Journal, Vol. 9.
I Web-applet: https://methodologycenter.shinyapps.io/PilotShiny/
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Clustered SMART Case Study:
Adaptive Implementation for School-based CBT
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Adaptive Implementation Intervention in Mental HealthPI: Kilbourne
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Sample Size Formulae for Cluster-Randomized SMARTsFor comparing 2 embedded AIs that begin with different treatments in a prototypicalSMART
N ≥ 4(z1−α/2+z1−β)2
m∗δ2 × (2− r)× (1 + (m − 1)ρ)
where
r = response rate after stage 1 treatment
m = avg number of SPs at each school
ρ = inter-school correlation in outcome
r = δ = 1/2, ρ = .03,m = 20, α = .05, need N = 100 for 80%power.Same question using standard 2-arm cluster trial requires N = 66.
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Sample Size Formulae for Cluster-Randomized SMARTsFor comparing 2 embedded AIs that begin with different treatments in a prototypicalSMART
N ≥ 4(z1−α/2+z1−β)2
m∗δ2 × (2− r)× (1 + (m − 1)ρ)
where
r = response rate after stage 1 treatment
m = avg number of SPs at each school
ρ = inter-school correlation in outcome
r = δ = 1/2, ρ = .03,m = 20, α = .05, need N = 100 for 80%power.Same question using standard 2-arm cluster trial requires N = 66.
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References
Kilbourne, Smith ... Almirall (2018). Adaptive School-based Implementationof CBT (ASIC): clustered-SMART for building an optimized adaptiveimplementation intervention to improve uptake of mental healthinterventions in schools., Implementation Science.
NeCamp, Kilbourne, Almirall (2017). Cluster-level adaptive interventionsand sequential, multiple assignment, randomized trials: Estimation andsample size considerations, Statistical Methods in Medical Research.
Kilbourne, Almirall, et al. (2014). Adaptive Implementation of EffectivePrograms Trial (ADEPT): cluster randomized SMART trial comparing astandard versus enhanced implementation strategy to improve outcomes of amood disorders program, Implementation Science.
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Myths or Misconceptions about Adaptive Interventions andSMARTs
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Myths or Misconceptions about Adaptive Interventions
Tailoring variables cannot differ based on previous intervention
An adaptive intervention must recommend a single interventioncomponent at each decision point
Adaptive interventions seek to replace clinical judgement
Adaptive interventions are only relevant in treatment settings
Adaptive interventions are non-standard because they involverandomization
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Interventions for Minimally Verbal Children with AutismPIs: Kasari(UCLA), Almirall(Mich), Kaiser(Vanderbilt), Smith(Rochester), Lord(Cornell)
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Myths or Misconceptions about SMART Studies
All research on adaptive interventions requires a SMART
SMARTs are an alternative to the RCT
SMARTs require prohibitively large sample sizes
All SMARTs require Multiple-Comparisons Adjustments
All SMARTs must include an embedded tailoring variable
All aspects of an embedded adaptive intervention must be randomized
SMARTs are a form of adaptive research design
SMARTs never include a control group
SMARTs require a “business as usual” control group
SMARTs require multiple consents
SMARTs are susceptible to high levels of study drop-out
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Interventions for Minimally Verbal Children with AutismPIs: Kasari(UCLA), Almirall(Mich), Kaiser(Vanderbilt), Smith(Rochester), Lord(Cornell)
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Myths or Misconceptions about SMART Studies
All research on adaptive interventions requires a SMART
SMARTs are an alternative to the RCT
SMARTs require prohibitively large sample sizes
All SMARTs require Multiple-Comparisons Adjustments
All SMARTs must include an embedded tailoring variable
All aspects of an embedded adaptive intervention must be randomized
SMARTs are a form of adaptive research design
SMARTs never include a control group
SMARTs require a “business as usual” control group
SMARTs require multiple consents
SMARTs are susceptible to high levels of study drop-out
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Multi-Tiered Systems of SupportPIs: Greg Roberts and Nathan Clemens (UT Austin)
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Multi-Tiered Systems of SupportPIs: Greg Roberts and Nathan Clemens (UT Austin)
Let’s call this ”Academic Adaptive Intervention.”
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Multi-Tiered Systems of SupportPIs: Greg Roberts and Nathan Clemens (UT Austin)
This study investigates the role of the self-regulation component (should itbe provided in stage 1, in stage 2, or at all?) in the context of theAcademic Adaptive Intervention.
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Multi-Tiered Systems of SupportPIs: Greg Roberts and Nathan Clemens (UT Austin)
I call this a “Seemingly-Restricted SMART”. Here, a 2x2 SMART design.
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What did you learn today?
You learned about Adaptive Interventions (in our literature,statisticians call these Dynamic Treatment Regimens)
You learned about some study designs for piloting and optimizingAdaptive Interventions
You learned about 5 SMART case studies (1 pilot, 3 standardSMART, 1 clustered SMART)
You learned about the sample size formulae for the repeated-measuresand clustered outcomes analyses
You learned about common misconceptions about AdaptiveInterventions and SMART
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Thank you! [email protected], http://www-personal.umich.edu/∼dalmiral/
Apply for next year’s IES training! http://d3lab-isr.com/training
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