A Multi-Task Learning Approach for Identifying Prognostic Variables Across Multiple Historical Clinical Trials.

In clinical trials, it is critical to incorporate prognostic variables to improve precision for estimating treatment effects. Per FDA guidance, these prognostic variables need to be prospectively specified in the statistical analysis plan. Therefore, there is a great need to effectively identify prognostic variables from historical studies that are similar to the new study. In this work, we propose a multi-task learning method to identify prognostic variables from multiple related historical clinical trials. Specifically, a bi-level constraint variable selection (Bi-CVS) method is developed to identify both study-specific (within-study) and program-level (cross-study) prognostic variables. In addition, we develop an efficient algorithm for the Bi-CVS, tailored for clinical trial settings, and investigate its consistency property. The performance of the proposed method is evaluated through simulations for various endpoints, including continuous, binary, and time-to-event. A real clinical application is also presented to illustrate the proposed method.