In clinical trials of longitudinal continuous outcomes, reference based imputation (RBI) has commonly been applied to handle missing outcome data in settings where the estimand incorporates the effects of intercurrent events, e.g. treatment discontinuation. RBI was originally developed in the multiple imputation framework, however recently conditional mean imputation (CMI) combined with the jackknife estimator of the standard error was proposed as a way to obtain deterministic treatment effect estimates and correct frequentist inference. For both multiple and CMI, a mixed model for repeated measures (MMRM) is often used for the imputation model, but this can be computationally intensive to fit to multiple data sets (e.g. the jackknife samples) and lead to convergence issues with complex MMRM models with many parameters. Therefore, a step-wise approach based on sequential linear regression (SLR) of the outcomes at each visit was developed for the imputation model in the multiple imputation framework, but similar developments in the CMI framework are lacking. In this article, we fill this gap in the literature by proposing a SLR approach to implement RBI in the CMI framework, and justify its validity using theoretical results and simulations. We also illustrate our proposal on a real data application.

翻译：在纵向连续结果的临床试验中，基于参考的插补（RBI）常用于处理估测目标包含并发事件（如治疗中断）影响时的缺失结局数据。RBI最初在多重插补框架中发展，但近期提出将条件均值插补（CMI）与刀切法标准误估计相结合，以获得确定性处理效应估计和正确频率学派推断。对于多重插补和CMI，重复测量混合模型（MMRM）常被用作插补模型，但拟合多个数据集（如刀切样本）时计算强度大，且复杂MMRM模型因参数众多易产生收敛问题。因此，虽已在多重插补框架中开发基于各访视结局序贯线性回归（SLR）的分步式插补建模方法，但CMI框架中缺乏类似进展。本文通过提出在CMI框架中实施RBI的SLR方法填补这一文献空白，并利用理论推导和模拟验证其有效性，同时通过真实数据应用展示该方法的实用性。