Browsing JRA Community by Author "Austin, Peter C."
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ItemGoodness-of-fit diagnostics for the propensity score model when estimating treatment effects using covariate adjustment with the propensity score(John Wiley & Sons, Ltd., 2008-10-29) Austin, Peter C.The propensity score is defined to be a subject’s probability of treatment selection, conditional on observed baseline covariates. Conditional on the propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. In the medical literature, there are three commonly employed propensity-score methods: stratification (subclassification) on the propensity score, matching on the propensity score, and covariate adjustment using the propensity score. Methods have been developed to assess the adequacy of the propensity score model in the context of stratification on the propensity score and propensity-score matching. However, no comparable methods have been developed for covariate adjustment using the propensity score. Inferences about treatment effect made using propensity-score methods are only valid if, conditional on the propensity score, treated and untreated subjects have similar distributions of baseline covariates. We develop both quantitative and qualitative methods to assess the balance in baseline covariates between treated and untreated subjects. The quantitative method employs the weighted conditional standardized difference. This is the conditional difference in the mean of a covariate between treated and untreated subjects, in units of the pooled standard deviation, integrated over the distribution of the propensity score. The qualitative method employs quantile regression models to determine whether, conditional on the propensity score, treated and untreated subjects have similar distributions of continuous covariates. We illustrate our methods using a large dataset of patients discharged from hospital with a diagnosis of a heart attack (acute myocardial infarction). The exposure was receipt of a prescription for a beta-blocker at hospital discharge. [Author Abstract] ItemMoving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies(John Wiley & Sons Ltd., 2015-07-09) Austin, Peter C.; Stuart, Elizabeth A.,The propensity score is defined as a subject’s probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalence, higher-order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. [Author Abstract]