Missing outcomes in randomized controlled trials are often handled by multiple imputation (MI). Rubin's rules are routinely used to estimate standard errors but can fail to provide valid standard error estimates for some commonly used procedures, such as reference-based imputation. We propose a one-step alternative by explicitly targeting the treatment effect implied by a given imputation model and constructing an efficient one-step estimator for that treatment effect via its influence function. Unlike Rubin's rules, this approach yields asymptotically valid inference. Moreover, the proposed method circumvents the stochastic component and computational burden of MI. We illustrate the approach with examples spanning a range of imputation models, including reference-based imputation and intercurrent-event-dependent imputation.

翻译：随机对照试验中的缺失结局常通过多重填补（MI）处理。鲁宾规则被常规用于估计标准误，但可能无法为某些常用程序（如基于参考的填补）提供有效的标准误估计。我们提出一种一步替代方案，通过明确针对给定填补模型隐含的治疗效应，并利用其影响函数构建该治疗效应的高效一步估计量。与鲁宾规则不同，该方法可产生渐近有效的推断。此外，所提方法规避了MI的随机成分与计算负担。我们通过涵盖一系列填补模型（包括基于参考的填补和依赖并发事件的填补）的实例来说明该方法。