Shortcomings of randomized clinical trials are pronounced in urgent health crises, when rapid identification of effective treatments is critical. Leveraging short-term surrogates in real-world data (RWD) can guide policymakers evaluating new treatments. In this paper, we develop novel estimators for the proportion of treatment effect (PTE) on the true outcome explained by a surrogate in RWD settings. We propose inverse probability weighted and doubly robust (DR) estimators of an optimal transformation of the surrogate and PTE by semi-nonparametrically modeling the relationship between the true outcome and surrogate given baseline covariates. We show that our estimators are consistent and asymptotically normal, and the DR estimator is consistent when either the propensity score model or outcome regression model is correctly specified. We compare our proposed estimators to existing estimators and show a reduction in bias and gains in efficiency through simulations. We illustrate the utility of our method in obtaining an interpretable PTE by conducting a cross-trial comparison of two biologic therapies for ulcerative colitis.

翻译：在紧急健康危机中,随机临床试验的缺陷十分明显,因为迅速确定有效治疗至关重要。在现实世界数据(RWD)中利用短期代用器可以指导决策者评估新的治疗。在本文中,我们为治疗效果对在RWD环境中代用器所解释的真正结果的比重(PTE)开发了新的估计值。我们提议了对代用和PTE进行最佳转换的偏重和双重强(DR)估计器,通过半非对称的模型来模拟真实结果和代用基准共差之间的关系。我们表明,我们的估量器是一致的,而且无损正常的,在正确指定了促动性评分模型或结果回归模型时,DR估计器是一致的。我们将我们提议的估量器与现有的估量器作比较,并表明通过模拟减少了偏差和效率的增益。我们的方法在获得可解释的PTE方面的效用是有用的,方法是对两种生物结交式疗法进行跨审比较。