Exploring Sensitive Biomarkers Associated With Short‐Term Responses and Long‐Term Outcomes Using Bayesian Additive Regression Trees

ABSTRACT Identifying good predictive baseline covariates to optimize the target population for a new treatment is an important aim in precision medicine. Early post‐baseline biomarker responses may serve as a supportive guide for physicians to decide whether to continue a treatment. We propose an exploratory two‐stage subgroup analysis method as a statistical tool to investigate the predictive value of such a short‐term response for the benefit of a new treatment in terms of long‐term outcomes. We use a flexible probability model, Bayesian additive regression trees (BART), to derive predictive conditional treatment effects (PCTE) based on a short‐term post‐baseline biomarker response using counterfactual modeling of responses to new and standard treatments for each patient. Constructing patient subgroups according to the PCTE, we analyze an observed long‐term outcome to identify a sensitive subpopulation. We carry out extensive simulation studies to examine the operating characteristics of the proposed method. For illustration, we apply the proposed method to data from a randomized clinical trial in patients with locally advanced or metastatic breast cancer. These findings support the predictive value of short‐term responses and provide a statistical approach for identifying patients expected to benefit from a new treatment in terms of a long‐term outcome.