The inverse probability weighted Cox model is frequently used to estimate the marginal hazard ratio. Its validity requires a crucial condition that the propensity score model be correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in the empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to compare the risk of postoperative hospitalization between sleeve gastrectomy and Roux‐en‐Y gastric bypass using data from a large medical claims and billing database. We further extend the development to multisite studies to enable each site to postulate multiple site‐specific propensity score models.

中文翻译：

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同时使用一组倾向评分模型估算边际风险比：多重稳健方法

逆概率加权Cox模型通常用于估计边际风险比。其有效性要求一个关键条件，即正确指定倾向得分模型。为了提供针对倾向得分模型的错误指定的保护，我们提出了一种基于经验似然理论的加权估计方法。所提出的估计器具有多重鲁棒性，因为当一组假定的得分模型包含正确指定的模型时，可以保证它是一致的。我们的仿真研究证明了所提方法在一致性和效率方面令人满意的有限样本性能。我们使用提议的方法，使用来自大型医疗索赔和计费数据库的数据，比较套管胃切除术和Roux-en-Y胃搭桥术术后住院的风险。我们进一步将开发扩展到多站点研究，以使每个站点都可以假设多个站点特定的倾向评分模型。