Propensity Scores in Pharmacoepidemiology: Beyond the Horizon.
Propensity Scores in Pharmacoepidemiology: Beyond the Horizon.
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DOI：
10.1007/s40471-017-0131-y
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发表时间：
2017-12
影响因子：
3.3
通讯作者：
Stuart EA
中科院分区：
文献类型：
--
作者：
Jackson JW;Schmid I;Stuart EA
在过去的十年中，倾向评分方法在药物流行病学中已经变得司空见惯。它们的采用面临着巨大的障碍，这些障碍来自药物流行病学对具有相当大的异质性和复杂性的大型医疗数据库的依赖。这些措施包括识别有临床意义的样本，定义治疗比较，并以尊重合理流行病学研究设计的方式测量协变量。额外的复杂性包括在医疗实践中面对变化时正确建模治疗决策，以及处理缺失信息和不可测量的混杂因素。在这篇综述中，我们研究了药物流行病学中的倾向评分方法的应用，特别注意这些和其他问题，着眼于实践标准，最近的方法学进展和未来发展的机会。倾向评分方法已经成熟，可以推进药物流行病学的比较有效性和安全性研究。这些包括分类处理的自然扩展，可以在给定设计约束的情况下优化样本量的匹配算法，渐近目标匹配和重叠样本的加权估计，以及结合机器学习来帮助协变量选择和模型构建。这些最新的令人鼓舞的进展应该通过模拟和实证研究进行进一步评估，但尽管如此，这仍然代表了治疗益处和危害的观察性研究的光明道路。
Propensity score methods have become commonplace in pharmacoepidemiology over the past decade. Their adoption has confronted formidable obstacles that arise from pharmacoepidemiology's reliance on large healthcare databases of considerable heterogeneity and complexity. These include identifying clinically meaningful samples, defining treatment comparisons, and measuring covariates in ways that respect sound epidemiologic study design. Additional complexities involve correctly modeling treatment decisions in the face of variation in healthcare practice, and dealing with missing information and unmeasured confounding. In this review, we examine the application of propensity score methods in pharmacoepidemiology with particular attention to these and other issues, with an eye towards standards of practice, recent methodological advances, and opportunities for future progress. Propensity score methods have matured in ways that can advance comparative effectiveness and safety research in pharmacoepidemiology. These include natural extensions for categorical treatments, matching algorithms that can optimize sample size given design constraints, weighting estimators that asymptotically target matched and overlap samples, and the incorporation of machine learning to aid in covariate selection and model building. These recent and encouraging advances should be further evaluated through simulation and empirical studies, but nonetheless represent a bright path ahead for the observational study of treatment benefits and harms.