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.

中文翻译：

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使用倾向评分评估治疗开始决策的效果：科学状况

混杂会在旨在评估因果关系的非实验研究中引起重大偏差。倾向评分方法可让研究人员根据接受治疗的可能性，通过在一个评分中汇总许多被测混杂因素的分布，来减少被测混杂因素造成的偏差。然后，可以使用此分数来减轻这些测量的混杂因素在接受目标治疗的人与比较人群中的分布之间的不平衡，从而减少对治疗效果的估计偏差。罗森鲍姆（Rosenbaum）和鲁宾（Rubin）在1983年正式采用了这种方法，从那时起，该方法已在各种科学学科中得到越来越多的使用。在这篇评论文章中，我们提供了在真实证据生成过程中倾向得分的概述，重点是在单项治疗决策（即在两种治疗方案之间进行选择）的设置中所使用的倾向得分。我们描述了倾向得分分析的五个方面：与潜在结果框架的一致性，对研究设计的影响，估计程序，实施方案和报告。我们通过强调所使用的比较器的类型，实现方法和余额评估技术如何随时间变化来为这些概念添加上下文。最后，我们讨论了倾向得分的不断发展的应用。对研究设计，估算程序，实施方案和报告的影响。我们通过强调使用的比较器类型，实现方法和余额评估技术随时间的变化，为这些概念添加了上下文。最后，我们讨论了倾向得分的不断发展的应用。对研究设计，估算程序，实施方案和报告的影响。我们通过强调所使用的比较器的类型，实现方法和余额评估技术如何随时间变化来为这些概念添加上下文。最后，我们讨论了倾向得分的不断发展的应用。