MRC Biostatistics Unit, University of Cambridge, IPH Forvie Site, Robinson Way, Cambridge, UK.
Centre for Dementia Prevention, University of Edinburgh, Kennedy Tower, UK.
Biostatistics. 2018 Oct 1;19(4):407-425. doi: 10.1093/biostatistics/kxx045.
Cohort data are often incomplete because some subjects drop out of the study, and inverse probability weighting (IPW), multiple imputation (MI), and linear increments (LI) are methods that deal with such missing data. In cohort studies of ageing, missing data can arise from dropout or death. Methods that do not distinguish between these reasons for missingness typically provide inference about a hypothetical cohort where no one can die (immortal cohort). It has been suggested that inference about the cohort composed of those who are still alive at any time point (partly conditional inference) may be more meaningful. MI, LI, and IPW can all be adapted to provide partly conditional inference. In this article, we clarify and compare the assumptions required by these MI, LI, and IPW methods for partly conditional inference on continuous outcomes. We also propose augmented IPW estimators for making partly conditional inference. These are more efficient than IPW estimators and more robust to model misspecification. Our simulation studies show that the methods give approximately unbiased estimates of partly conditional estimands when their assumptions are met, but may be biased otherwise. We illustrate the application of the missing data methods using data from the 'Origins of Variance in the Old-old' Twin study.
队列数据通常是不完整的，因为一些研究对象退出了研究，逆概率加权（Inverse Probability Weighting，简称 IPW）、多重插补（Multiple Imputation，简称 MI）和线性递补（Linear Increments，简称 LI）是处理此类缺失数据的方法。在老龄化队列研究中，缺失数据可能是由于退出或死亡导致的。通常，不区分缺失原因的方法会提供关于没有人可以死亡的假设队列（不朽队列）的推断。有人建议，对任何时间点仍然存活的队列（部分条件推断）进行推断可能更有意义。MI、LI 和 IPW 都可以进行调整以提供部分条件推断。本文澄清并比较了这些 MI、LI 和 IPW 方法对连续结果进行部分条件推断所需的假设。我们还提出了增强型 IPW 估计量以进行部分条件推断。与 IPW 估计量相比，这些估计量更有效，对模型误设也更稳健。我们的模拟研究表明，当这些方法的假设得到满足时，它们会对部分条件估计值给出近似无偏的估计，但在其他情况下可能会有偏差。我们使用“老年双胞胎研究中的方差起源”数据说明了缺失数据方法的应用。
