Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, USA.
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.
Stat Med. 2021 Feb 28;40(5):1224-1242. doi: 10.1002/sim.8837. Epub 2021 Jan 6.
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.
逆概率加权 Cox 模型常用于估计边缘危险比。其有效性需要一个关键条件，即倾向评分模型得到正确的指定。为了防止倾向评分模型的指定错误，我们提出了一种基于经验似然理论的加权估计方法。所提出的估计量是多重稳健的，即在一组假定的倾向评分模型中包含一个正确指定的模型时，它可以保证一致性。我们的模拟研究表明，在所提出的方法在一致性和效率方面具有令人满意的有限样本性能。我们应用所提出的方法，使用来自大型医疗索赔和计费数据库的数据，比较袖状胃切除术和 Roux-en-Y 胃旁路术的术后住院风险。我们进一步将开发扩展到多站点研究，以使每个站点都能假定多个特定站点的倾向评分模型。
