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Bayesian Methods for Comparative Effectiveness Research with Observational Data

Award Number: R01GM111339
ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 03/01/2015
PERIOD OF PERFORMANCE END DATE: 01/31/2020

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Bayesian Methods for Comparative Effectiveness Research with Observational Data - 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.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2020 ( Subtotal = -$12,151 )
2020	2018	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	25 SHATTUCK ST	BOSTON	MA	02115	SUFFOLK	USA	Biomedical Research and Research Training	000	4	6/3/2020	NON-COMPETING CONTINUATION	-$12,151
														Subtotal = -$12,151

Issue Date FY: 2018 ( Subtotal = $640,927 )
2018	2018	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	25 SHATTUCK ST	BOSTON	MA	02115	SUFFOLK	USA	Biomedical Research and Research Training	001	4	5/9/2018	NON-COMPETING CONTINUATION	$64,093
2018	2018	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	25 SHATTUCK ST	BOSTON	MA	02115	SUFFOLK	USA	Biomedical Research and Research Training	000	4	1/19/2018	NON-COMPETING CONTINUATION	$576,834
														Subtotal = $640,927

Issue Date FY: 2017 ( Subtotal = $640,308 )
2017	2017	PRESIDENT AND FELLOWS OF HARVARD COLLEGE	25 SHATTUCK ST	BOSTON	MA	02115	SUFFOLK	USA	Biomedical Research and Research Training	001	3	6/6/2017	NON-COMPETING CONTINUATION	$64,031
2017	2017	HARVARD COLLEGE, PRESIDENT & FELLOWS OF	25 SHATTUCK ST	BOSTON	MA	02115	SUFFOLK	USA	Biomedical Research and Research Training	000	3	1/21/2017	NON-COMPETING CONTINUATION	$576,277
														Subtotal = $640,308

Issue Date FY: 2016 ( Subtotal = $668,275 )
2016	2016	HARVARD UNIVERSITY MEDICAL SCHOOL	643 HUNTINGTON AVENUE	BOSTON	MA	02115	SUFFOLK	USA	Biomedical Research and Research Training	000	2	1/28/2016	NON-COMPETING CONTINUATION	$601,448
2016	2016	HARVARD UNIVERSITY MEDICAL SCHOOL	643 HUNTINGTON AVENUE	BOSTON	MA	02115	SUFFOLK	USA	Biomedical Research and Research Training	001	2	2/11/2016	NON-COMPETING CONTINUATION	$66,827
														Subtotal = $668,275

Issue Date FY: 2015 ( Subtotal = $559,641 )
2015	2015	HARVARD UNIVERSITY MEDICAL SCHOOL	643 HUNTINGTON AVENUE	BOSTON	MA	02115	SUFFOLK	USA	Biomedical Research and Research Training	000	1	2/27/2015	NEW	$559,641
														Subtotal = $559,641

Grand Total All Awards = $2,497,000

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Bayesian Methods for Comparative Effectiveness Research with Observational Data

Award Number: R01GM111339

ORGANIZATION: NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 03/01/2015

PERIOD OF PERFORMANCE END DATE: 01/31/2020

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