Faucett Cheryl L, Schenker Nathaniel, Taylor Jeremy M G
Department of Biostatistics, UCLA School of Public Health, Los Angeles, California 90095-1772, USA.
Biometrics. 2002 Mar;58(1):37-47. doi: 10.1111/j.0006-341x.2002.00037.x.
We develop an approach, based on multiple imputation, to using auxiliary variables to recover information from censored observations in survival analysis. We apply the approach to data from an AIDS clinical trial comparing ZDV and placebo, in which CD4 count is the time-dependent auxiliary variable. To facilitate imputation, a joint model is developed for the data, which includes a hierarchical change-point model for CD4 counts and a time-dependent proportional hazards model for the time to AIDS. Markov chain Monte Carlo methods are used to multiply impute event times for censored cases. The augmented data are then analyzed and the results combined using standard multiple-imputation techniques. A comparison of our multiple-imputation approach to simply analyzing the observed data indicates that multiple imputation leads to a small change in the estimated effect of ZDV and smaller estimated standard errors. A sensitivity analysis suggests that the qualitative findings are reproducible under a variety of imputation models. A simulation study indicates that improved efficiency over standard analyses and partial corrections for dependent censoring can result. An issue that arises with our approach, however, is whether the analysis of primary interest and the imputation model are compatible.
我们开发了一种基于多重填补的方法，用于在生存分析中利用辅助变量从删失观测值中恢复信息。我们将该方法应用于一项比较齐多夫定（ZDV）和安慰剂的艾滋病临床试验数据，其中CD4细胞计数是随时间变化的辅助变量。为便于进行填补，针对该数据构建了一个联合模型，其中包括一个针对CD4细胞计数的分层变点模型和一个针对艾滋病发病时间的随时间变化的比例风险模型。采用马尔可夫链蒙特卡罗方法对删失病例的事件时间进行多重填补。然后对扩充后的数据进行分析，并使用标准的多重填补技术合并结果。将我们的多重填补方法与仅分析观测数据的方法进行比较表明，多重填补会使ZDV估计效应产生微小变化，并减小估计标准误。敏感性分析表明，在各种填补模型下定性结果具有可重复性。模拟研究表明，与标准分析相比，该方法可提高效率，并能对相依删失进行部分校正。然而，我们的方法存在一个问题，即主要关注的分析与填补模型是否兼容。
