Cutting Feedback in Bayesian Regression Adjustment for the Propensity Score

McCandless Lawrence C, Douglas Ian J., Evans Stephen J. and Smeeth Liam
Additional contact information
McCandless Lawrence C: Simon Fraser University
Douglas Ian J.: London School of Hygiene & Tropical Medicine
Evans Stephen J.: London School of Hygiene & Tropical Medicine
Smeeth Liam: London School of Hygiene & Tropical Medicine

The International Journal of Biostatistics, 2010, vol. 6, issue 2, 24

Abstract: McCandless, Gustafson and Austin (2009) describe a Bayesian approach to regression adjustment for the propensity score to reduce confounding. A unique property of the method is that the treatment and outcome models are combined via Bayes theorem. However, this estimation procedure can be problematic if the outcome model is misspecified. We observe feedback that can bias propensity score estimates. Building on new innovation in Bayesian computation, we propose a technique for cutting feedback in a Bayesian propensity analysis. We use the posterior distribution of the propensity scores as an input in the regression model for the outcome. The method is approximately Bayesian in the sense that it does not use the full likelihood for estimation. Nonetheless, it severs feedback between the treatment and outcome giving propensity score estimates that are free from bias but modeled with uncertainty. We illustrate the method in a matched cohort study investigating the effect of statins on primary stroke prevention.

Keywords: confounding; bias; observational studies; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (14)

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DOI: 10.2202/1557-4679.1205

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