subtee: An R Package for Subgroup Treatment Effect Estimation in Clinical Trials

Abstract

The investigation of subgroups is an integral part of randomized clinical trials. Exploration of treatment effect heterogeneity is typically performed by covariate-adjusted analyses including treatment-by-covariate interactions. Several statistical techniques, such as model averaging and bagging, were proposed recently to address the problem of selection bias in treatment effect estimates for subgroups. In this paper, we describe the subtee R package for subgroup treatment effect estimation. The package can be used for all commonly encountered type of outcomes in clinical trials (continuous, binary, survival, count). We also provide additional functions to build the subgroup variables to be used and to plot the results using forest plots. The functions are demonstrated using data from a clinical trial investigating a treatment for prostate cancer with a survival endpoint.