Heterogeneity in treatment effects: Can modelling techniques provide personalized prediction of treatment response and uncover groups of respondents?

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Randomized trials of novel drugs, health technologies and non-drug interventions are usually designed to test average treatment effects. In the analysis of these trials it is therefore typically assumed that treatment effects are constant across individuals. This strong assumption is often not supported by empirical evidence or the theories under-girding the intervention. While heterogeneity in intervention effects is often expected, the assessment of differential treatment effects is limited by available statistical methods. This proposal aims to develop and compare two different statistical methods to investigate how these differential effect can best be identified and examines the ability of these approaches to predict an individual's response to treatment. The work will utilize three recently finished studies in three important medical areas: Stroke, epilepsy and physical activity which enables methodological work, simulations, and applied analyses to be carried out simultaneously creating a feedback loop in which methodological problems that arise in applied analyses are identified and then can be addressed through statistical and simulation work, improving each. The multidisciplinary research team includes experienced substantive experts for each study as well as methodologists with experience in statistical methods for randomized studies.