Nonparametric Benefit-Risk Assessment Using Marker Processes in the Presence of a Terminal Event

• Activity Number: 364
• Type: Contributed
• Date/Time: Tuesday, August 11, 2015 : 10:30 AM to 12:20 PM
• Sponsor: Section on Risk Analysis
• Author(s): Yifei Sun* and Chiung-Yu Huang and Mei-Cheng Wang
• Companies: The Johns Hopkins University and The Johns Hopkins University and The Johns Hopkins University
• Keywords: Longitudinal Marker Process ; Survival Analysis ; Kernel Smoothing ; Multiple Event Process ; Induced Informative Censoring

Abstract

Benefit-risk assessment is a crucial step in the medical decision process. In many biomedical studies, both longitudinal marker measurements and time to a terminal event serve as important endpoints for benefit-risk assessment. The effect of an intervention or a treatment on the longitudinal marker process can be in conflict with its effect on the time to the terminal event. Thus questions arise on how to evaluate treatment effects based on the two endpoints. We present a unified framework for benefit-risk assessment using the observed longitudinal markers and time to event data. We propose a cumulative weighted marker process to synthesize information from the two endpoints, and use its mean function at a pre-specified time point as a benefit-risk summary measure. We consider nonparametric estimation of the summary measure under two scenarios: (i) the longitudinal marker is measured intermittently, and (ii) the value of the longitudinal marker is observed throughout the entire follow-up period. The large-sample properties of the estimators are derived. Simulation studies and the application to an AIDS clinical trial exhibit that the proposed methods are reliable for practical use.