Abstract
Recent advances in longitudinal methodologies for observational studies have contributed to a better understanding of Autism as a neurodevelopmental condition characterized by within-person and between-person variability over time across behavioral domains. However, this finer-grained approach to the study of developmental variability has yet to be applied to Autism intervention science. The widely adopted experimental designs in the field—randomized control trials and quasi-experimental designs—hold value for inferring treatment effects; at the same time, they are limited in elucidating what works for whom, why, and when, given the idiosyncrasies of neurodevelopmental disorders where predictors and outcomes are often dynamic in nature. This perspective paper aims to serve as a primer for Autism intervention scientists to rethink the way we approach predictors of treatment response and treatment-related change using a dynamic lens. We discuss several empirical gaps, and potential methodological challenges and opportunities pertaining to: (1) capturing finer-grained treatment effects in specific behavioral domains as indexed by micro-level within-person changes during and beyond intervention; and (2) examining and modeling dynamic prediction of treatment response. Addressing these issues can contribute to enhanced study designs and methodologies that generate evidence to inform the development of more personalized interventions and stepped care approaches for individuals on the heterogeneous spectrum of Autism with changing needs across development.
Highlights
Over the past decade, the field of Autism intervention science has made significant advances that led to promising evidence on improving the developmental outcomes of individuals with Autism [1]
Establishing prediction models with only baseline predictors may under-utilize the available information and limit predictive predictor-outcome relation, as contrasted with the common practice of studying this interplay as static in Autism intervention science, we suggest that future research may want to identify time-varying predictors or covariates of treatment outcomes based on theory and existing evidence
We discussed here two important empirical gaps in Autism intervention science that, to date, has been relying on observational or experimental designs predominantly characterized by: [1] evaluating treatment effects based on group comparisons of mean pre-post changes in general outcome domains; and [2] studying prediction of treatment outcomes as a static phenomenon
Summary
The field of Autism intervention science has made significant advances that led to promising evidence on improving the developmental outcomes of individuals with Autism [1]. Even in the case of predictors that are invariant in nature (e.g., sex), the magnitude of their predictive effect may still vary across the course of intervention and/or development [e.g., interactions between sex and age for comorbid symptoms in children with Autism; [33]] All these complexities regarding the prediction of treatment-related change point to the need for more “time-sensitive” approaches, such as dynamic prediction modeling that allows for examining time-varying effects on treatment outcomes (see Figure 2). Autism researchers could apply a similar approach to explore research questions related to co-occurring symptoms (e.g., repetitive and restricted behaviors and anxiety) during behavioral interventions, parent-child dyads (e.g., parent responsiveness and child’s joint attention skills) during parentmediated interventions, or inclusion of predictors that may change in nature (e.g., cognitive and language skills) This would allow for elucidating how and when the covariates of interest contribute to treatment targets or interact with intervention elements at an individual level
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