Team: Ellie Van Vogt, Suzie Cro, Victoria Cornelius

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Traditional methods for subgroup analysis include univariate analysis of interactions coupled with thresholding or restricting and repeating the analysis in pre-defined subgroups. The availability of historical trial data and the advancement of machine learning methods means that we are now able to take a data-driven approach to subgroup analysis, and search for characteristics that define heterogenous treatment effects.

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We use causal machine learning approaches to estimate the conditional average treatment effect (CATE) in historic RCTs. We employ the causal forest for this estimation and examine how the CATE varies over predictors to determine how to define subgroups of heterogeneity. Meta-learning algorithms combine several machine learning algorithms, where the output the models are trying to estimate is the individual treatment effect or the average treatment effect.

 

The post-hoc determination of “super-responders” in positive result trials or of positive responders in null result trials can form recommendations for future research.

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