The Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, University of Washington, Seattle.
KNG Health Consulting, LLC, North Bethesda, MD.
J Manag Care Spec Pharm. 2023 Apr;29(4):391-399. doi: 10.18553/jmcp.2023.29.4.391.
Many approaches to propensity score methods are used in the applied health economics and outcomes research literature. Often this creates confusion when different approaches produce different results for the same data. To present a conceptual overview based on a potential outcomes framework to demonstrate how more than 1 mean treatment effect parameter can be estimated using the propensity score methods and how the selection of appropriate methods should align with the scientific questions. We highlight that more than 1 mean treatment effect parameter can be estimated using the propensity score methods. Using the potential outcomes framework and alternate data-generating processes, we discuss under what assumptions different mean treatment effect parameter estimates are supposed to vary. We tie these discussions with propensity score methods to show that different approaches may estimate different parameters. We illustrate these methods using a case study of the comparative effectiveness of apixaban vs warfarin on the likelihood of stroke among patients with a prior diagnosis of atrial fibrillation. Different mean treatment effect parameters take on different values when treatment effects are heterogeneous. We show that traditional propensity score approaches, such as blocking, weighting, matching, or doubly robust, can estimate different mean treatment effect parameters. Therefore, they may not produce the same results even when applied to the same data using the same covariates. We found significant differences in our case study estimates of mean treatment effect parameters. Still, once a mean treatment effect parameter is targeted, estimates across different methods are not different. This highlights the importance of first selecting the target parameter for analysis by aligning the interpretation of the target parameter with the scientific questions and then selecting the specific method to estimate this target parameter. We present a conceptual overview of propensity score methods in health economics and outcomes research from a potential outcomes framework. We hope these discussions will help applied researchers choose appropriate propensity score approaches for their analysis. Dr Unuigbe's time was supported through an unrestricted postdoctoral fellowship from Pfizer to the University of Washington, Seattle.
在应用健康经济学和结果研究文献中，有许多倾向评分方法的方法。当不同的方法对相同的数据产生不同的结果时，这常常会引起混淆。为了基于潜在结果框架提出一个概念概述，以展示如何使用倾向评分方法估计超过 1 个平均治疗效果参数，以及如何选择合适的方法与科学问题保持一致。我们强调，使用倾向评分方法可以估计超过 1 个平均治疗效果参数。我们使用潜在结果框架和替代数据生成过程，讨论在什么假设下，不同的平均治疗效果参数估计值应该有所不同。我们将这些讨论与倾向评分方法联系起来，以表明不同的方法可能会估计不同的参数。我们使用一个关于阿哌沙班与华法林在有先前心房颤动诊断的患者中风可能性方面的比较疗效的案例研究来说明这些方法。当治疗效果存在异质性时，不同的平均治疗效果参数会取不同的值。我们表明，传统的倾向评分方法，如分层、加权、匹配或双重稳健，都可以估计不同的平均治疗效果参数。因此，即使将相同的协变量应用于相同的数据，它们也可能不会产生相同的结果。我们在案例研究中发现了平均治疗效果参数估计值的显著差异。尽管如此，一旦目标是平均治疗效果参数，那么不同方法的估计值就不会有所不同。这突出了首先通过将目标参数的解释与科学问题保持一致来选择分析的目标参数，然后选择估计该目标参数的特定方法的重要性。我们从潜在结果框架出发，在健康经济学和结果研究中提出了倾向评分方法的概念概述。我们希望这些讨论将帮助应用研究人员为他们的分析选择合适的倾向评分方法。Unuigbe 博士的时间得到了辉瑞公司向华盛顿大学西雅图分校提供的一项无限制博士后奖学金的支持。
