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A propensity score is the probability that a participant is assigned to the treatment group based on a set of baseline covariates. Propensity scores provide an excellent basis for equating treatment groups on a large set of covariates when randomization is not possible. This article provides a nontechnical introduction to propensity scores for clinical researchers. If all important covariates are measured, then methods that equate on propensity scores can achieve balance on a large set of covariates that mimics that achieved by a randomized experiment. We present an illustration of the steps in the construction and checking of propensity scores in a study of the effectiveness of a health coach versus treatment as usual on the well-being of seriously ill individuals. We then consider alternative methods of equating groups on propensity scores and estimating treatment effects including matching, stratification, weighting, and analysis of covariance. We illustrate a sensitivity analysis that can probe for the potential effects of omitted covariates on the estimate of the causal effect. Finally, we briefly consider several practical and theoretical issues in the use of propensity scores in applied settings. Propensity score methods have advantages over alternative approaches to equating groups particularly when the treatment and control groups do not fully overlap, and there are nonlinear relationships between covariates and the outcome. [ABSTRACT FROM AUTHOR]

Abstract: Stress and HIV disproportionately affect Latinx adults in the United States, contributing to numerous health inequities. Among people living with HIV (PLWH), increased stress is associated with decreased medication adherence, effectiveness, and higher rates of immunosuppression and comorbidities. Our study ( N = 126) examined the relationship between perceived stress and HIV-related health among Latinx ( n = 83) and non-Latinx White ( n = 43) PLWH. Latinx PLWH reported better HIV-related health outcomes than non-Latinx White PLWH, despite higher viral load (all p 's < .05). An interaction effect showed that higher perceived distress was associated with worse health outcomes and lower CD4+ T cell count only in Latinx PLWH ( p 's < .05). Our findings highlight the consequences of stress on HIV-related health. Notably, higher stress may be especially detrimental among Latinx PLWH, underscoring the need for more culturally tailored interventions in HIV-related care. Future research should include additional sociocultural factors and longitudinal assessment in a larger sample., (Copyright © 2024 The Authors. Published by Wolters Kluwer Health, Inc. on behalf of Association of Nurses in AIDS Care.)