Inference with combined data from subgroup selection and validation phases in clinical trials

When a promising subgroup is identified from an unsuccessful trial with a broad target population, we often need to evaluate and possibly confirm the selected subgroup with a follow-up study, typically a validation trial, on the subgroup. In this paper we focus on the panitumumab study and ask the question of how to utilize data from both trials to improve the efficiency of subgroup evaluation without selection bias there. We propose a new resampling-based approach to quantify and remove selection bias and then to perform data combination from both trials for valid and efficient inference on the subgroup effect. The proposed method is model-free and asymptotically sharp. We apply the proposed method to analyze the panitumumab trial and show how much data combination could help improve the analysis of clinical trials when a promising subgroup is identified from part of the data and accelerate the delivery of new treatment to the patients in need.