Shire, Lexington, Massachusetts.
Stat Med. 2019 May 10;38(10):1715-1733. doi: 10.1002/sim.8062. Epub 2018 Dec 18.
An efficient monotone data augmentation (MDA) algorithm is proposed for missing data imputation for incomplete multivariate nonnormal data that may contain variables of different types and are modeled by a sequence of regression models including the linear, binary logistic, multinomial logistic, proportional odds, Poisson, negative binomial, skew-normal, skew-t regressions, or a mixture of these models. The MDA algorithm is applied to the sensitivity analyses of longitudinal trials with nonignorable dropout using the controlled pattern imputations that assume the treatment effect reduces or disappears after subjects in the experimental arm discontinue the treatment. We also describe a heuristic approach to implement the controlled imputation, in which the fully conditional specification method is used to impute the intermediate missing data to create a monotone missing pattern, and the missing data after dropout are then imputed according to the assumed nonignorable mechanisms. The proposed methods are illustrated by simulation and real data analyses. Sample SAS code for the analyses is provided in the supporting information.
提出了一种高效的单调数据扩充（MDA）算法，用于对可能包含不同类型变量的不完全多元非正态数据进行缺失数据插补，这些数据通过一系列回归模型进行建模，包括线性、二项逻辑、多项逻辑、比例优势、泊松、负二项、偏态正态、偏态 t 回归，或这些模型的混合。该 MDA 算法应用于使用控制模式插补进行不可忽略缺失的纵向试验的敏感性分析，这些控制模式插补假设在实验臂中的受试者停止治疗后，治疗效果会降低或消失。我们还描述了一种启发式方法来实现控制插补，其中使用完全条件指定方法来插补中间缺失数据以创建单调缺失模式，然后根据假设的不可忽略机制对辍学后的缺失数据进行插补。所提出的方法通过模拟和真实数据分析进行说明。分析的示例 SAS 代码在支持信息中提供。
