Department of Psychiatry, University of Vermont, 1 South Prospect Street, Burlington, VT 05401, USA.
Psychol Methods. 2010 Sep;15(3):234-49. doi: 10.1037/a0019623.
There is considerable interest in using propensity score (PS) statistical techniques to address questions of causal inference in psychological research. Many PS techniques exist, yet few guidelines are available to aid applied researchers in their understanding, use, and evaluation. In this study, the authors give an overview of available techniques for PS estimation and PS application. They also provide a way to help compare PS techniques, using the resulting measured covariate balance as the criterion for selecting between techniques. The empirical example for this study involves the potential causal relationship linking early-onset cannabis problems and subsequent negative mental health outcomes and uses data from a prospective cohort study. PS techniques are described and evaluated on the basis of their ability to balance the distributions of measured potentially confounding covariates for individuals with and without early-onset cannabis problems. This article identifies the PS techniques that yield good statistical balance of the chosen measured covariates within the context of this particular research question and cohort.
人们对使用倾向评分 (PS) 统计技术来解决心理研究中的因果推理问题非常感兴趣。有许多 PS 技术,但很少有指南可以帮助应用研究人员理解、使用和评估这些技术。在这项研究中,作者概述了可用的 PS 估计和 PS 应用技术。他们还提供了一种帮助比较 PS 技术的方法,使用得到的测量协变量平衡作为在技术之间进行选择的标准。本研究的实证例子涉及早期大麻问题与随后的负面心理健康结果之间的潜在因果关系,并使用前瞻性队列研究的数据。根据它们在有和没有早期大麻问题的个体中平衡测量潜在混杂协变量分布的能力来描述和评估 PS 技术。本文确定了在特定研究问题和队列背景下,能够很好地平衡所选测量协变量的 PS 技术。