倾向值匹配研究.ppt

2007Jan05 GCRC Research-Skills Workshop How to use propensity scores in the analysis of nonrandomized designs Patrick G. Arbogast Department of Biostatistics Vanderbilt University Medical Center Motivation Randomized clinical trials: randomization guarantees that on avg no systematic differences in observed/unobserved covariates. Observational studies: no control over tx assignments, and E+/E- groups may have large differences in observed covariates. Can adjust for this via study design (matching) or during estimation of tx effect (stratification/regression). Analysis limitations 10 events/variable (EPV), estimated reg coeff’s may be biased SE’s may be incorrect (Peduzzi et al, 1996). Simulation study for logistic reg. Harrell et al (1985) also advocates min no. of EPV. A solution: propensity scores (Rosenbaum Rubin, 1983). Likelihood that patient receives E+ given risk factors. Intuition Covariate is confounder only if its distribution in E+/E- differ. Consider 1-factor matching: low-dose aspirin mortality. Age, a strong confounder, can be controlled by matching. Can extend to many risk factors, but becomes cumbersome. Propensity scores provide a summary measure to control for multiple confounders simultaneously. Propensity score estimation Identify potential confounders. Current conventional wisdom: if uncertain whether covariate is confounder, include it. Model E+ (typically dichotomous) as function of covariates using entire cohort. E+ is outcome for propensity score estimation. Do not include D+. Logistic reg typically used. Propensity score = estimated Pr(E+|covariates). Counterintuitive? Natural question: why estimate probability that a patient receives E+ since we already know exposure status? Answer: adjusting observed E+ with probability of E+ (“propensity”) creates a “quasi-randomized” experiment. For E+ E- patients with same propensity score, can imagine they were “randomly” assigned to each group. Subjects in E+/E- groups with equal (or nearly equ