Sun Yanqing, Gilbert Peter B, McKeague Ian W
University of North Carolina at Charlotte, University of Washington and Fred Hutchinson Cancer Research Center, and Columbia University.
Ann Stat. 2009 Feb 1;37(1):394-426. doi: 10.1214/07-AOS554.
For time-to-event data with finitely many competing risks, the proportional hazards model has been a popular tool for relating the cause-specific outcomes to covariates [Prentice et al. Biometrics34 (1978) 541-554]. This article studies an extension of this approach to allow a continuum of competing risks, in which the cause of failure is replaced by a continuous mark only observed at the failure time. We develop inference for the proportional hazards model in which the regression parameters depend nonparametrically on the mark and the baseline hazard depends nonparametrically on both time and mark. This work is motivated by the need to assess HIV vaccine efficacy, while taking into account the genetic divergence of infecting HIV viruses in trial participants from the HIV strain that is contained in the vaccine, and adjusting for covariate effects. Mark-specific vaccine efficacy is expressed in terms of one of the regression functions in the mark-specific proportional hazards model. The new approach is evaluated in simulations and applied to the first HIV vaccine efficacy trial.
对于具有有限多个竞争风险的生存时间数据，比例风险模型一直是将特定病因结局与协变量相关联的常用工具[普伦蒂斯等人，《生物统计学》34 (1978) 541 - 554]。本文研究了这种方法的一种扩展，以允许存在连续的竞争风险，其中失败原因被仅在失败时刻观测到的连续标记所取代。我们针对比例风险模型进行推断，其中回归参数非参数地依赖于标记，且基线风险非参数地依赖于时间和标记两者。这项工作的动机是在评估HIV疫苗效力时，需要考虑试验参与者中感染的HIV病毒与疫苗中所含HIV毒株的基因差异，并对协变量效应进行调整。标记特异性疫苗效力通过标记特异性比例风险模型中的一个回归函数来表示。新方法在模拟中进行了评估，并应用于首个HIV疫苗效力试验。
