摘要：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.
关键词：propensity score; covariate balance; observational studies; association analysis; HELP Clinic; proc glm; proc logistic; cat.psa; box.psa
