Speaker
Description
Assessment of treatment effect heterogeneity is a challenging problem 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. While the original plot purposely avoids inferential statistics, we believe that there could be interest in guiding the interpretation of the observed heterogeneity. For example whether the observed heterogeneity is expected or larger than expected under global homogeneity. In this presentation, we will introduce a computationally efficient method to derive homogeneity regions in such exhaustive subgroup treatment effect plots based on the double robust learner approach (Kennedy, 2023). We also conduct a comprehensive simulation study to evaluate the validity of the approach and illustrate the methodology with a real case study.
References:
Kennedy, E.H., 2023. Towards optimal doubly robust estimation of heterogeneous causal effects. Electronic Journal of Statistics Vol. 17 (2023) 3008-3049.
Muysers, C., Dmitrienko, A., Kulmann, H., Kirsch, B., Lippert, S., Schmelter, T., Schulz, A., Mentenich, N., Schmitz, H., Schaefers, M., Meinhardt, G., Keil, T., Roll, S., 2020. A Systematic
Approach for Post Hoc Subgroup Analyses With Applications in Clinical Case Studies. Ther Innov Regul Sci 54, 507.518.
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