Wen Lan, Seaman Shaun R
MRC Biostatistics Unit, University of Cambridge, IPH Forvie Site, Robinson Way, Cambridge CB2 0SR, U.K.
Biometrics. 2018 Dec;74(4):1427-1437. doi: 10.1111/biom.12891. Epub 2018 May 17.
We propose semi-parametric methods to model cohort data where repeated outcomes may be missing due to death and non-ignorable dropout. Our focus is to obtain inference about the cohort composed of those who are still alive at any time point (partly conditional inference). We propose: i) an inverse probability weighted method that upweights observed subjects to represent subjects who are still alive but are not observed; ii) an outcome regression method that replaces missing outcomes of subjects who are alive with their conditional mean outcomes given past observed data; and iii) an augmented inverse probability method that combines the previous two methods and is double robust against model misspecification. These methods are described for both monotone and non-monotone missing data patterns, and are applied to a cohort of elderly adults from the Health and Retirement Study. Sensitivity analysis to departures from the assumption that missingness at some visit t is independent of the outcome at visit t given past observed data and time of death is used in the data application.
我们提出了半参数方法来对队列数据进行建模，其中重复的结局可能因死亡和不可忽略的失访而缺失。我们的重点是对在任何时间点仍存活的人群组成的队列进行推断（部分条件推断）。我们提出：i）一种逆概率加权方法，该方法对观察到的受试者进行加权，以代表仍存活但未被观察到的受试者；ii）一种结局回归方法，该方法用给定过去观察数据的条件均值结局替代存活受试者的缺失结局；iii）一种增强逆概率方法，该方法结合了前两种方法，并且对模型误设具有双重稳健性。这些方法针对单调和非单调缺失数据模式进行了描述，并应用于来自健康与退休研究的老年人群队列。在数据应用中，我们使用了敏感性分析，以检验与以下假设的偏离情况：在某个访视t时的失访与给定过去观察数据和死亡时间的访视t时的结局无关。
