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

• 成果类型: Article
• 署名作者: Sun, Yifei; Huang, Chiung-Yu; Wang, Mei-Cheng
• 署名单位: Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health; Johns Hopkins University; Johns Hopkins Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1180988
• 发表日期: 2017
• 页码: 826-836
• 关键词: quality-adjusted survival; cost-effectiveness analysis; longitudinal data; time; estimators; HEALTH; sample; TRIAL; aids

摘要

Benefitrisk assessment is a crucial step in medical decision process. In many biomedical studies, both longitudinal marker measurements and time to a terminal event serve as important endpoints for benefitrisk assessment. The effect of an intervention or a treatment on the longitudinal marker process, however, 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, for the purpose of deciding on which treatment is most likely to benefit the patients. In this article, we present a unified framework for benefitrisk 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 prespecified time point as a benefitrisk summary measure. We consider nonparametric estimation of the summary measure under two scenarios: (i) the longitudinal marker is measured intermittently during the study period, 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 and compared. Simulation studies and data examples exhibit that the proposed methods are easy to implement and reliable for practical use. Supplemental materials for this article are available online.