Inference for Events With Dependent Risks in Multiple Endpoint Studies
针对多时间-事件终点研究中事件间风险可能相依的情况，提出用Kaplan-Meier和累积发生率估计量的简单函数进行非参数估计，并推导渐近分布理论，为方差估计和两样本检验提供方法。
Abstract Abstract Kaplan–Meier and cumulative incidence functions are not sufficient descriptive devices for studies that have multiple time-to-event endpoints. For example, in cancer treatment research the probability of tumor recurrence conditional on not having died from treatment-related toxicities and the prevalence of graft-versus-host disease among leukemia-free patients surviving a bone marrow transplant are of interest. These quantities can be estimated nonparametrically using simple functions of several Kaplan–Meier and cumulative incidence estimates for events with possibly dependent risks. We derive asymptotic distribution theory for such functions by representing Kaplan–Meier, cumulative incidence, and cumulative hazard estimators as sums of iid random variables. Variance estimation also follows directly from this representation. Two-sample test statistics with asymptotic null distribution theory are presented. Several examples illustrate the utility of these results. Key Words: Competing risksCumulative hazardCumulative incidenceKaplan–Meier