Liu Danping, Yeung Edwina H, McLain Alexander C, Xie Yunlong, Buck Louis Germaine M, Sundaram Rajeshwari
Biostatistics and Bioinformatics Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health.
Epidemiology Branch, Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD.
Paediatr Perinat Epidemiol. 2017 Sep;31(5):468-478. doi: 10.1111/ppe.12382. Epub 2017 Aug 2.
Imperfect follow-up in longitudinal studies commonly leads to missing outcome data that can potentially bias the inference when the missingness is nonignorable; that is, the propensity of missingness depends on missing values in the data. In the Upstate KIDS Study, we seek to determine if the missingness of child development outcomes is nonignorable, and how a simple model assuming ignorable missingness would compare with more complicated models for a nonignorable mechanism.
To correct for nonignorable missingness, the shared random effects model (SREM) jointly models the outcome and the missing mechanism. However, the computational complexity and lack of software packages has limited its practical applications. This paper proposes a novel two-step approach to handle nonignorable missing outcomes in generalized linear mixed models. We first analyse the missing mechanism with a generalized linear mixed model and predict values of the random effects; then, the outcome model is fitted adjusting for the predicted random effects to account for heterogeneity in the missingness propensity.
Extensive simulation studies suggest that the proposed method is a reliable approximation to SREM, with a much faster computation. The nonignorability of missing data in the Upstate KIDS Study is estimated to be mild to moderate, and the analyses using the two-step approach or SREM are similar to the model assuming ignorable missingness.
The two-step approach is a computationally straightforward method that can be conducted as sensitivity analyses in longitudinal studies to examine violations to the ignorable missingness assumption and the implications relative to health outcomes.
纵向研究中不完善的随访通常会导致结局数据缺失，当缺失情况不可忽略时，这可能会使推断产生偏差；也就是说，缺失的倾向取决于数据中的缺失值。在纽约州北部儿童研究中，我们试图确定儿童发育结局的缺失是否不可忽略，以及一个假设可忽略缺失的简单模型与针对不可忽略机制的更复杂模型相比如何。
为了校正不可忽略的缺失，共享随机效应模型（SREM）对结局和缺失机制进行联合建模。然而，计算复杂性和缺乏软件包限制了其实际应用。本文提出了一种新颖的两步法来处理广义线性混合模型中不可忽略的缺失结局。我们首先用广义线性混合模型分析缺失机制并预测随机效应的值；然后，对结局模型进行拟合，调整预测的随机效应以考虑缺失倾向中的异质性。
广泛的模拟研究表明，所提出的方法是对SREM的可靠近似，计算速度要快得多。纽约州北部儿童研究中缺失数据的不可忽略性估计为轻度到中度，使用两步法或SREM进行的分析与假设可忽略缺失的模型相似。
两步法是一种计算简单的方法，可在纵向研究中作为敏感性分析进行，以检验对可忽略缺失假设的违背情况以及与健康结局相关的影响。
