Review: propensity score methods with application to the HELP clinic clinical study

Abstract

Review: propensity score methods with application to the HELP clinic clinical study Shesh N Rai,1,2,* Xiaoyong Wu,1,* Deo K Srivastava,3 John A Craycroft,2 Jayesh P Rai,4 Sanjay Srivastava,4 Robert F James,5 Maxwell Boakye,5 Aruni Bhatnagar,4 Richard Baumgartner6 1Biostatistics Shared Facility, James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA; 2Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, USA; 3Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, USA; 4Division of Cardiology, University of Louisville, Louisville, KY, USA; 5Department of Neurosurgery, University of Louisville, Louisville, KY, USA; 6Department of Epidemiology and Population Health, University of Louisville, KY, USA *These authors contributed equally to this work Abstract: Observational studies, common in clinical trials, often suffer from a lack of random assignment of the treatment. This can lead to large differences in covariates between the treated and untreated groups, which should be accounted for prior to inference, hypothesis tests, etc. Propensity score methods are frequently used to control for potentially confounding covariates when assessing causal effects of treatment on outcome. In this review, we introduce four adjustment methods based on propensity scores including matching, stratification, inverse probability of treatment weighting and covariate adjustment. Also, we give a general description of these four methods and provide some visual tools to assess covariate balance between the treated and untreated groups. We confirm the feasibility of propensity score methods by analyzing the Health Evaluation and Linkage to Primary care clinic clinical data. Keywords: propensity score, covariate balance, observational studies, association analysis, HELP Clinic, proc glm, proc logistic, cat.psa, box.psa