An Efficient Multiple Imputation Algorithm for Control-Based and Delta-Adjusted Pattern Mixture Models using SAS

In clinical trials, mixed effect models for repeated measures (MMRM) and pattern mixture models (PMM) are often used to analyze longitudinal continuous outcomes. We describe a simple missing data imputation algorithm for the MMRM that can be easily implemented in standard statistical software packages such as SAS PROC MI. We explore the relationship of the missing data distribution in the control-based and delta-adjusted PMMs with that in the MMRM, and suggest an efficient imputation algorithm for these PMMs. The unobserved values in PMMs can be imputed by subtracting the mean difference in the posterior predictive distributions of missing data from the imputed values in MMRM. We also suggest a modification of the copy reference imputation procedure to avoid the possibility that after dropout, subjects from the active treatment arm will have better mean response trajectory than subjects who stay on the active treatment. The proposed methods are illustrated by the analysis of an antidepressant trial. Supplementary materials for this article are available online.在临床试验中，重复测量混合效应模型（mixed effect models for repeated measures，缩写MMRM）与模式混合模型（pattern mixture models，缩写PMM）常被用于分析纵向连续结局指标。本文介绍一种针对MMRM的简易缺失数据插补算法，该算法可轻松在SAS PROC MI等标准统计软件包中实现。我们探究了基于对照的缺失数据分布、经差值调整的PMM与MMRM中缺失数据分布之间的关联，并针对这类PMM提出了高效的插补算法。PMM中的未观测值可通过将MMRM的插补值减去缺失数据后验预测分布中的均数差值来插补。我们还对拷贝参考插补流程进行了修正，以避免出现以下情形：试验脱落后，活性治疗组受试者的平均应答轨迹优于仍留在该组接受治疗的受试者。本文所提出的方法通过一项抗抑郁试验的分析进行了演示，本文的补充材料可在线获取。