Department of Epidemiology, UNC Chapel Hill, Chapel Hill, North Carolina, USA.
Research Department of Practice and Policy, UCL School of Pharmacy, London, UK.
Stat Med. 2021 Mar 30;40(7):1718-1735. doi: 10.1002/sim.8866. Epub 2020 Dec 29.
Confounding can cause substantial bias in nonexperimental studies that aim to estimate causal effects. Propensity score methods allow researchers to reduce bias from measured confounding by summarizing the distributions of many measured confounders in a single score based on the probability of receiving treatment. This score can then be used to mitigate imbalances in the distributions of these measured confounders between those who received the treatment of interest and those in the comparator population, resulting in less biased treatment effect estimates. This methodology was formalized by Rosenbaum and Rubin in 1983 and, since then, has been used increasingly often across a wide variety of scientific disciplines. In this review article, we provide an overview of propensity scores in the context of real-world evidence generation with a focus on their use in the setting of single treatment decisions, that is, choosing between two therapeutic options. We describe five aspects of propensity score analysis: alignment with the potential outcomes framework, implications for study design, estimation procedures, implementation options, and reporting. We add context to these concepts by highlighting how the types of comparator used, the implementation method, and balance assessment techniques have changed over time. Finally, we discuss evolving applications of propensity scores.
混杂因素可能会导致非实验性研究中因果效应的估计产生很大偏差。倾向评分方法允许研究人员通过基于治疗接受概率将许多测量混杂因素的分布总结到一个单一的评分中，从而减少由测量混杂因素引起的偏差。然后，该评分可以用于减轻治疗组和对照组之间这些测量混杂因素分布的不平衡，从而得出偏差较小的治疗效果估计值。这种方法由 Rosenbaum 和 Rubin 于 1983 年正式提出，此后，已在广泛的科学领域中越来越多地被使用。在这篇综述文章中，我们将在真实世界证据生成的背景下介绍倾向评分，并重点介绍其在单一治疗决策中的使用，即两种治疗选择之间的选择。我们描述了倾向评分分析的五个方面：与潜在结果框架的一致性、对研究设计的影响、估计程序、实施选项和报告。我们通过强调随着时间的推移，比较器的类型、实施方法和平衡评估技术的变化，为这些概念提供了背景。最后，我们讨论了倾向评分的不断发展的应用。
