Bayesian Methods for Comparative Effectiveness Research with Observational Data

Harvard Medical School, Boston MA

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Abstract

? DESCRIPTION (provided by applicant): Comparative effectiveness research (CER) relies upon the analysis of a rapidly expanding universe of observational data made possible by the growing integration of health care delivery, the dissemination of electronic medical records systems, and the development of clinical registries data. These data present both extraordinary opportunities for research aimed at improving value in health care as well as new challenges for meaningful investigation. A critical barrier relates to the lack of sound statistical methods and tools that can address the multiple facets of estimating treatment effects in observational studies when treatment effectiveness may vary across subpopulations and covariate information defining these subgroups is high dimensional and sometimes unmeasured. Aim 1 develops new Bayesian methods for causal inference in large observational data to 1) estimate average causal effects accounting for model uncertainty in the selection of measured confounders and 2) estimate average causal effects in sub-populations accounting for uncertainty in the selection of the subgroups. This newly proposed approach generalizes existing methods because it will not rely on the specification of a single model, but instead will estimate parameters by averaging across several models. Aim 2 develops new Bayesian methods for assessing treatment effects in the presence of unmeasured confounders that moderate treatment effects in large observational data. The new approach uses instrumental variables to 1) identify the distributions, rather than means, of essential causal parameters and 2) link causal parameters to subgroups by systematically relaxing selection bias assumptions. Aim 3 applies the new methods to observational studies to provide new and fully reproducible knowledge in the areas of medical devices, surgical procedures, and pharmaceutical treatments. Aim 4 develops flexible, efficient, robust, well documented, user-friendly R libraries and SAS macros, facilitatin dissemination of our newly developed methods. Our new methods, their applications to large administrative and clinical registry data, and their dissemination will allow the entire research community to address modern CER questions with the highest methodological rigor.