Sun Yifei, Huang Chiung-Yu, Wang Mei-Cheng
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD.
J Am Stat Assoc. 2017;112(518):826-836. doi: 10.1080/01621459.2016.1180988. Epub 2017 Apr 12.
Benefit-risk 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 benefit-risk 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 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 prespecified 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 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.
效益风险评估是医学决策过程中的关键一步。在许多生物医学研究中，纵向标志物测量和至终点事件的时间均作为效益风险评估的重要终点。然而，一种干预或治疗对纵向标志物过程的影响可能与其对至终点事件时间的影响相冲突。因此，就如何基于这两个终点评估治疗效果以确定哪种治疗最有可能使患者受益的问题便应运而生。在本文中，我们提出了一个使用观察到的纵向标志物和事件时间数据进行效益风险评估的统一框架。我们提出一个累积加权标志物过程来整合来自这两个终点的信息，并将其在预定时间点的均值函数用作效益风险汇总指标。我们考虑在两种情形下对汇总指标进行非参数估计：（i）在研究期间间歇性地测量纵向标志物；（ii）在整个随访期内观察纵向标志物的值。推导并比较了估计量的大样本性质。模拟研究和数据实例表明，所提出的方法易于实施且在实际应用中可靠。本文的补充材料可在线获取。
