Landmark Proportional Subdistribution Hazards Models for Dynamic Prediction of Cumulative Incidence Probabilities

• Activity Number: 173
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Qing Liu*+ and Chung-Chou Chang
• Companies: University of Pittsburgh and University of Pittsburgh
• Keywords: Competing risks ; dynamic prediction ; landmark analysis ; proportional subdistribution hazards ; risk prediction ; time-varying effect

Abstract

A risk predictive model that can dynamically predict an individual's cause-specific cumulative incidence probabilities is crucial to risk stratification, drug effect evaluation and treatment assignment. For data containing no competing risks, landmark Cox models have served for this purpose. In this study, we extended the landmark method to the Fine-Gray proportional subdistribution hazards (PSH) model for data with competing risks. The proposed landmark PSH model is robust against the violation of the PSH assumption and can directly estimate the conditional cumulative incidence probabilities at a fixed landmark point. We further developed a more comprehensive landmark PSH supermodel which enables the user to complete a dynamic prediction involving a number of landmark points in one step. Through simulations we evaluated the prediction performance of the proposed landmark PSH models by estimating the time-dependent Brier scores. The proposed models were applied to a breast cancer trial to predict the dynamic cumulative incidence probabilities of developing locoregional recurrence.