Analyses of cumulative incidence functions via non-parametric multiple imputation
Analyses of cumulative incidence functions via non-parametric multiple imputation
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DOI：
10.1002/sim.3402
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发表时间：
2008-11-29
影响因子：
2
通讯作者：
Gray, Robert J.
中科院分区：
文献类型：
--
作者：
Ruan, Ping K.;Gray, Robert J.
我们描述了一种非参数多重补偿方法，该方法从竞争风险失效次数中恢复丢失的潜在截尾信息，用于累积关联函数的分析。该方法可应用于分层分析、时变协变量分析、病例队列样本加权分析和聚类生存数据分析等现有方法难以实现的场合。该方法使用Kaplan-Meier推算方法对截尾次数进行推算，从而形成推算数据集，从而可以使用针对普通右截尾生存数据开发的技术和软件来分析累积发病率。我们讨论了该方法，并从模拟和实际数据实例中证明了该方法产生了有效的估计和良好的性能。该方法可以很容易地通过可用的软件实现，具有较小的编程要求(对于归责步骤)。它为竞争风险数据累积发生率的复杂分析提供了一种实用的替代分析工具。版权所有(C)2008 John Wiley&Sons，Ltd.
We describe a non-parametric multiple imputation method that recovers the missing potential censoring information from competing risks failure times for the analysis of cumulative incidence functions. The method can be applied in the settings of stratified analyses, time-varying covariates, weighted analysis of case-cohort samples and clustered survival data analysis, where no current available methods can be readily implemented. The method uses a Kaplan-Meier imputation method for the censoring times to form an imputed data set, SO Cumulative incidence can be analyzed using techniques and software developed for ordinary right censored survival data. We discuss the methodology and show from both simulations and real data examples that the method yields valid estimates and performs well. The method can be easily implemented via available software with a minor programming requirement (for the imputation step). It provides a practical, alternative analysis tool for otherwise complicated analyses of cumulative incidence of competing risks data. Copyright (C) 2008 John Wiley & Sons, Ltd.