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，参考假设插补（RBI）已广泛应用于处理伴发事件（如治疗中断）效应需纳入目标量的场景中的缺失结局数据。RBI最初在多重插补框架中建立，但近期提出了条件均值插补（CMI）与刀切法标准误估计相结合的方法，以获得确定性治疗效果估计及正确的频率学推断。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，但该模型拟合多个数据集（如刀切样本）时计算复杂，且参数众多的复杂MMRM模型易导致收敛问题。为此，在多重插补框架中已开发出基于每次访视结果序贯线性回归（SLR）的逐步插补方法，但CMI框架中类似方法尚属空白。本文通过提出SLR方法实现CMI框架下的RBI填补这一文献空白，并利用理论结果和模拟验证其有效性，同时通过真实数据应用展示所提方法。