Randomized controlled trials (RCTs) are a cornerstone of comparative effectiveness because they remove the confounding bias present in observational studies. However, RCTs are typically much smaller than observational studies because of financial and ethical considerations. Therefore it is of great interest to be able to incorporate plentiful observational data into the analysis of smaller RCTs. Previous estimators developed for this purpose rely on unrealistic additional assumptions without which the added data can bias the effect estimate. Recent work proposed an alternative method (prognostic adjustment) that imposes no additional assumption and increases efficiency in the analysis of RCTs. The idea is to use the observational data to learn a prognostic model: a regression of the outcome onto the covariates. The predictions from this model, generated from the RCT subjects' baseline variables, are used as a covariate in a linear model. In this work, we extend this framework to work when conducting inference with nonparametric efficient estimators in trial analysis. Using simulations, we find that this approach provides greater power (i.e., smaller standard errors) than without prognostic adjustment, especially when the trial is small. We also find that the method is robust to observed or unobserved shifts between the observational and trial populations and does not introduce bias. Lastly, we showcase this estimator leveraging real-world historical data on a randomized blood transfusion study of trauma patients.

翻译：随机对照试验（RCT）是比较效果研究的基石，因其能消除观察性研究中的混杂偏倚。然而，由于经济与伦理考量，RCT的规模通常远小于观察性研究。因此，如何将丰富的观察性数据纳入小型RCT的分析中具有重要意义。既往针对此目标开发的估计器依赖于不切实际的额外假设，否则引入数据会偏倚效应估计。近期研究提出了一种无需额外假设的替代方法（预后调整），可在RCT分析中提升效率。其核心思想是：利用观察性数据学习预后模型（即结果变量对协变量的回归），并将该模型在RCT受试者基线变量上的预测值作为线性模型中的协变量。本研究将该框架扩展至使用非参数高效估计器进行试验统计推断的场景。模拟实验表明，该方法相较于未进行预后调整的情况具有更高统计效能（即更小的标准误），尤其当试验规模较小时更为显著。此外，该方法对观察性人群与试验人群之间观测到或未观测到的偏移具有稳健性，且不会引入偏倚。最后，我们通过一项创伤患者随机输血研究的真实世界历史数据，展示了该估计器的应用效果。