Assessing of global evidence against homogeneity for subgroup Treatment Effect Plots

Music City Center 

Subgroup analyses present significant challenges in biostatistics, particularly in clinical trials. Estimation of treatment effects within subgroups in an exploratory setting is often unreliable due to limited sample sizes and multiplicity issues. Through the past decades, many efforts have been made to address this problem. Among them, Muysers et al. (2020) considered generating a graphical display that presents numerous subgroups on the same figure and could potentially illustrate homogeneity or heterogeneity. This interactive plot has outcome variable (treatment effect measure) on the y-axis and subgroup size on the x-axis. We refer to this plot as an exhaustive subgroup treatment effect plot. As the original plot avoids inferential statistics, there is still a need for a global assessment of whether the observed heterogeneity is expected or larger than expected under global homogeneity. In this presentation, we will introduce a computationally efficient method to create the exhaustive subgroup treatment effect plot and derive the confidence regions that control the familywise error rate at a pre-specified significance level, which can also serve as a global interaction test. The methodology uses the double robust learner approach (Kennedy, 2023) and employs high-dimensional integration to directly compute quantiles of the cumulative distribution function of the maximum statistics to obtain the rejection region. We also conduct a comprehensive simulation study to evaluate the validity of the approach and benchmark it with other approaches.

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