Liu G Frank, Pang Lei
a Late Development Clinical Biostatistics, Merck Research Laboratories , North Wales , Pennsylvania , USA.
J Biopharm Stat. 2016;26(5):924-36. doi: 10.1080/10543406.2015.1094810. Epub 2015 Sep 29.
Reference-based imputation (RBI) methods have been proposed as sensitivity analyses for longitudinal clinical trials with missing data. The RBI methods multiply impute the missing data in treatment group based on an imputation model built using data from the reference (control) group. The RBI will yield a conservative treatment effect estimate as compared to the estimate obtained from multiple imputation (MI) under missing at random (MAR). However, the RBI analysis based on the regular MI approach can be overly conservative because it not only applies discount to treatment effect estimate but also posts penalty on the variance estimate. In this article, we investigate the statistical properties of RBI methods, and propose approaches to derive accurate variance estimates using both frequentist and Bayesian methods for the RBI analysis. Results from simulation studies and applications to longitudinal clinical trial datasets are presented.
基于参考值的插补（RBI）方法已被提出作为对存在缺失数据的纵向临床试验进行敏感性分析的方法。RBI方法基于使用来自参考（对照）组的数据构建的插补模型，对治疗组中的缺失数据进行多重插补。与在随机缺失（MAR）情况下从多重插补（MI）获得的估计相比，RBI将产生一个保守的治疗效果估计。然而，基于常规MI方法的RBI分析可能过于保守，因为它不仅对治疗效果估计进行折扣，还对方差估计施加惩罚。在本文中，我们研究了RBI方法的统计特性，并提出了使用频率主义和贝叶斯方法进行RBI分析以得出准确方差估计的方法。给出了模拟研究结果以及在纵向临床试验数据集上的应用结果。
