Sequential linear regression for conditional mean imputation of longitudinal continuous outcomes under reference-based assumptions
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参考组假设下纵向连续结局条件均值插补的序贯线性回归
- 研究问题
- 研究纵向临床试验在停药等事件后出现结局缺失时，如何以参考组插补假设实施条件均值插补，并降低重复拟合的计算困难。
- 方法与思路
- 作者用各访视结局的序贯线性回归实现参考组条件均值插补，针对既有重复测量混合模型方案的计算和收敛问题提出替代。
- 材料陈述的贡献
- 摘要称填补了序贯线性回归在条件均值插补框架中的方法空缺，通过理论结果和模拟论证有效性，并提供真实数据示例。
- 阅读关注点
- 应核对参考组假设对应的目标效应、序贯模型与混合模型的关系，以及插补规则、标准误计算和理论有效性的条件。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收均未核实；摘要未说明缺失假设细节和计算收益，不能据此判断插补假设是否可信。
原文摘要
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.