Franklin Jessica M, Eddings Wesley, Austin Peter C, Stuart Elizabeth A, Schneeweiss Sebastian
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, U.S.A.
Institute for Clinical Evaluative Sciences, Toronto, Canada.
Stat Med. 2017 May 30;36(12):1946-1963. doi: 10.1002/sim.7250. Epub 2017 Feb 16.
Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of events observed in a small number of patients. In addition, studies constructed from electronic healthcare databases, for example, administrative claims data, often adjust for many, possibly hundreds, of potential confounders. Despite the importance of maximizing efficiency when there are many confounders and few observed outcome events, there has been relatively little research on the relative performance of different propensity score methods in this context. In this paper, we compare a wide variety of propensity-based estimators of the marginal relative risk. In contrast to prior research that has focused on specific statistical methods in isolation of other analytic choices, we instead consider a method to be defined by the complete multistep process from propensity score modeling to final treatment effect estimation. Propensity score model estimation methods considered include ordinary logistic regression, Bayesian logistic regression, lasso, and boosted regression trees. Methods for utilizing the propensity score include pair matching, full matching, decile strata, fine strata, regression adjustment using one or two nonlinear splines, inverse propensity weighting, and matching weights. We evaluate methods via a 'plasmode' simulation study, which creates simulated datasets on the basis of a real cohort study of two treatments constructed from administrative claims data. Our results suggest that regression adjustment and matching weights, regardless of the propensity score model estimation method, provide lower bias and mean squared error in the context of rare binary outcomes. Copyright © 2017 John Wiley & Sons, Ltd.
基于电子医疗数据库的治疗非随机研究对于提供做出明智治疗决策所需的证据至关重要，但通常依赖于比较少数患者中观察到的事件发生率。此外，从电子医疗数据库构建的研究，例如行政索赔数据，通常会对许多可能多达数百个的潜在混杂因素进行调整。尽管在存在许多混杂因素且观察到的结局事件较少时提高效率很重要，但在这种情况下，关于不同倾向评分方法的相对性能的研究相对较少。在本文中，我们比较了多种基于倾向评分的边际相对风险估计器。与先前孤立地关注特定统计方法而不考虑其他分析选择的研究不同，我们将一种方法定义为从倾向评分建模到最终治疗效果估计的完整多步骤过程。所考虑的倾向评分模型估计方法包括普通逻辑回归、贝叶斯逻辑回归、套索回归和增强回归树。利用倾向评分的方法包括配对匹配、完全匹配、十分位数分层、精细分层、使用一个或两个非线性样条的回归调整、逆倾向加权和匹配权重。我们通过“血浆模型”模拟研究评估方法，该研究基于一项由行政索赔数据构建的关于两种治疗的真实队列研究创建模拟数据集。我们的结果表明，无论倾向评分模型估计方法如何，回归调整和匹配权重在罕见二元结局的情况下提供较低的偏差和均方误差。版权所有© 2017约翰威立父子有限公司。
