Statistics Colloquium, Dr. Guoqing Diao

Title: Semiparametric Regression Analysis for Composite Endpoints Subject To Component-wise Censoring

Abstract:  Composite endpoints with censored data are commonly used as study outcomes in clinical trials. For example, progression free survival is a widely used composite endpoint with disease progression and death as the two components. Progression free survival time is often defined as the time from randomization to the earlier occurrence of disease progression or death from any cause. The censoring times of the two components could be different for patients not experiencing the endpoint event. Conventional approaches, such as taking the minimum of the censoring times of the two components as the censoring time for progression free survival time, may suffer from efficiency loss and could potentially produce biased estimate of the treatment effect. In this article, we propose a new likelihood-based approach that decomposes the endpoints and models both the progression free survival time and time from disease progression to death. In this new approach, the censoring time for different components will be distinguished and linked to individual components. The proposed approach makes full use of available information and provides a direct and improved estimate of the treatment effect on progression free survival time. Extensive simulation studies demonstrate that the proposed method outperforms several conventional approaches and is robust to various model misspecifications. An application to a prostate cancer clinical trial is provided