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Activity Number: 277 - Statistical Methods for Composite Time-To-Event Endpoints
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Lifetime Data Science Section
Abstract #300330
Title: Semiparametric Regression Analysis for Composite Endpoints Subject to Component-Wise Censoring
Author(s): Guoqing Diao* and Donglin Zeng and Chunlei Ke and Haijun Ma and Qi Jiang and Joseph G Ibrahim
Companies: George Mason University and UNC Chapel Hill and Biogen and Amgen Inc. and Amgen and UNC
Keywords: Composite endpoint; Hazard ratio; Progression-free survival; Semiparametric model

Composite endpoints such as progression-free survival with censored data are commonly used as study outcomes in clinical trials. The censoring times of the two components of progression-free survival 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 produce biased estimates of the treatment effect. We propose a new likelihood-based approach that decomposes the endpoints and models both the progression-free survival time and the time from disease progression to death. The censoring times for different components are distinguished. The approach makes full use of available information and provides a direct and improved estimate of the treatment effect on progression-free survival time. Simulations demonstrate that the proposed method outperforms several other approaches and is robust against various model misspecifications. An application to a prostate cancer clinical trial is provided.

Authors who are presenting talks have a * after their name.

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