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.

Public Health Relevance

Health information growth has created unprecedented opportunities to evaluate treatment effectiveness in large and broadly representative patient populations but where the benefits of treatments may vary across population subgroups. We will develop novel statistical methods for estimating causal effects that (a) account for uncertainty in the selection of subgroups and for selection of measured confounders; and (b) accommodate unmeasured confounders that moderate treatment effects, in settings where the number of confounders is large and where no randomization has occurred. We will illustrate the new methods to answer several substantive questions raised in our ongoing interdisciplinary collaborations that motivate our methods development.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM111339-02
Application #
9024579
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Marcus, Stephen
Project Start
2015-03-01
Project End
2019-01-31
Budget Start
2016-02-01
Budget End
2017-01-31
Support Year
2
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Harvard Medical School
Department
Administration
Type
Schools of Medicine
DUNS #
047006379
City
Boston
State
MA
Country
United States
Zip Code
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Spertus, Jacob V; T Normand, Sharon-Lise; Wolf, Robert et al. (2016) Assessing Hospital Performance After Percutaneous Coronary Intervention Using Big Data. Circ Cardiovasc Qual Outcomes 9:659-669

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