Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.
Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York, USA.
Biometrics. 2022 Sep;78(3):1168-1180. doi: 10.1111/biom.13482. Epub 2021 May 18.
In disease settings where study participants are at risk for death and a serious nonfatal event, composite endpoints defined as the time until the earliest of death or the nonfatal event are often used as the primary endpoint in clinical trials. In practice, if the nonfatal event can only be detected at clinic visits and the death time is known exactly, the resulting composite endpoint exhibits "component-wise censoring." The standard method used to estimate event-free survival in this setting fails to account for component-wise censoring. We apply a kernel smoothing method previously proposed for a marker process in a novel way to produce a nonparametric estimator for event-free survival that accounts for component-wise censoring. The key insight that allows us to apply this kernel method is thinking of nonfatal event status as an intermittently observed binary time-dependent variable rather than thinking of time to the nonfatal event as interval-censored. We also propose estimators for the probability in state and restricted mean time in state for reversible or irreversible illness-death models, under component-wise censoring, and derive their large-sample properties. We perform a simulation study to compare our method to existing multistate survival methods and apply the methods on data from a large randomized trial studying a multifactor intervention for reducing morbidity and mortality among men at above average risk of coronary heart disease.
在研究参与者有死亡和严重非致命事件风险的疾病环境中，复合终点通常被定义为死亡或非致命事件最早发生的时间，作为临床试验的主要终点。在实践中，如果非致命事件只能在诊所就诊时检测到，并且确切知道死亡时间，则产生的复合终点表现出“分量式删失”。在这种情况下，用于估计无事件生存的标准方法未能考虑分量式删失。我们以一种新颖的方式应用了先前为标记过程提出的核平滑方法，以产生一种非参数估计器，用于考虑分量式删失的无事件生存。使我们能够应用这种核方法的关键见解是，将非致命事件状态视为间歇性观察的二元时变变量，而不是将到非致命事件的时间视为区间删失。我们还针对可逆或不可逆疾病-死亡模型，在分量式删失下，提出了状态概率和状态受限均值的估计量，并推导出它们的大样本性质。我们进行了一项模拟研究，将我们的方法与现有的多状态生存方法进行比较，并将这些方法应用于一项大型随机试验的数据，该试验研究了一种多因素干预措施，以降低冠心病高危男性的发病率和死亡率。
