One approach for increasing the efficiency of randomized trials is the use of "external controls" -- individuals who received the control treatment in the trial during routine practice or in prior experimental studies. Existing external control methods, however, can have substantial bias if the populations underlying the trial and the external control data are not exchangeable. Here, we characterize a randomization-aware class of treatment effect estimators in the population underlying the trial that remain consistent and asymptotically normal when using external control data, even when exchangeability does not hold. We consider two members of this class of estimators: the well-known augmented inverse probability weighting trial-only estimator, which is the efficient estimator when only trial data are used; and a more efficient member of the class when exchangeability holds and external control data are available, which we refer to as the optimized randomization-aware estimator. To achieve robust integration of external control data in trial analyses, we then propose a combined estimator based on the efficient trial-only estimator and the optimized randomization-aware estimator. We show that the combined estimator is consistent and no less efficient than the most efficient of the two component estimators, whether the exchangeability assumption holds or not. We examine the estimators' performance in simulations and we illustrate their use with data from two trials of paliperidone extended-release for schizophrenia.

翻译：提高随机化试验效率的一种方法是使用“外部对照”——即在常规实践或先前实验研究中接受试验对照治疗的个体。然而，如果试验基础人群与外部对照数据所代表的人群不可互换，现有外部对照方法可能存在显著偏倚。本文刻画了一类在试验基础人群中具有随机化意识的处理效应估计量，即使不可互换性假设不成立，在使用外部对照数据时仍能保持一致性且渐近正态。我们考虑该类估计量中的两个成员：广为人知的仅使用试验数据时的有效估计量——增强逆概率加权纯试验估计量；以及在可互换性成立且可获得外部对照数据时该类中更有效的成员——我们称之为优化随机化意识估计量。为实现试验分析中外部对照数据的稳健整合，我们随后提出一种基于有效纯试验估计量与优化随机化意识估计量的组合估计量。我们证明无论可互换性假设是否成立，该组合估计量均具有一致性，且其效率不低于两个分量估计量中最高效者。我们通过模拟检验了各估计量的性能，并利用帕利哌酮缓释片治疗精神分裂症的两项试验数据进行了实证演示。