Return-to-baseline is an important method to impute missing values or unobserved potential outcomes when certain hypothetical strategies are used to handle intercurrent events in clinical trials. Current return-to-baseline approaches seen in literature and in practice inflate the variability of the "complete" dataset after imputation and lead to biased mean estimators {when the probability of missingness depends on the observed baseline and/or postbaseline intermediate outcomes}. In this article, we first provide a set of criteria a return-to-baseline imputation method should satisfy. Under this framework, we propose a novel return-to-baseline imputation method. Simulations show the completed data after the new imputation approach have the proper distribution, and the estimators based on the new imputation method outperform the traditional method in terms of both bias and variance, when missingness depends on the observed values. The new method can be implemented easily with the existing multiple imputation procedures in commonly used statistical packages.
翻译:在临床试验中,当使用某些假设战略处理周期间事件时,返回到基线是估算缺失值或未观察到的潜在结果的一个重要方法。在文献和实践中,目前的返回到基线方法扩大了估算后“完整”数据集的可变性,并导致偏差平均估计值(当缺失概率取决于观察到的基线和/或基线后中间结果时)。在本条中,我们首先提供一套标准,即返回到基线的估算方法应当满足。在这个框架内,我们提议一种新的返回到基线的估算方法。模拟显示在新的估算方法得到适当分布后完成的数据,基于新的估算方法的估算值在偏差和差异方面都超越了传统方法,而缺失取决于观察到的值。在常用的统计软件包中,新的方法可以很容易地应用。