Shire, 300 Shire Way, Lexington, MA 02421, USA.
Stat Med. 2018 Apr 30;37(9):1467-1481. doi: 10.1002/sim.7583. Epub 2018 Jan 15.
The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods.
受控插补方法是指近年来在处理不可忽略缺失的纵向临床试验中常用的一类模式混合模型。这些模式混合模型假设在脱落的实验臂参与者与对照组参与者具有相似的反应模式，或者比否则相似的继续接受实验治疗的参与者有更差的结局。尽管它很流行，但受控插补方法尚未为纵向二分类和有序结局正式开发，部分原因是缺乏此类终点的自然多元分布。在本文中，我们提出了两种基于序贯逻辑回归和多元概率比模型的二分类和有序数据的受控插补方法。利用序贯逻辑回归的单调数据扩充技术和多元概率比模型的参数扩展单调数据扩充方案，为缺失数据插补开发了有效的马尔可夫链蒙特卡罗算法。我们通过模拟和精神分裂症临床试验的分析来评估所提出的程序的性能，并将其与完全条件指定、末次观察结转和基线观察结转插补方法进行比较。
