Laura Faden Garabedian, PhD; Paula Chu, MS; Sengwee Toh, ScD; Alan M. Zaslavsky, PhD; Stephen B. Soumerai, ScD
Note: This project was awarded first place at the 2012 Association for Public Policy Analysis and Management Fall Research Conference Poster Awards.
Disclaimer: The authors of this article are responsible for its content. Statements in the article should not be construed as endorsement by the Agency for Healthcare Research and Quality or the U.S. Department of Health and Human Services.
Acknowledgment: The authors thank Sebastian Schneeweiss, MD, ScD, and John Seeger, PharmD, DrPH, both from the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, for their insightful feedback on the study design and manuscript and Elizabeth Garry, MPH, from the Division of Pharmacoepidemiology and Pharmacoeconomics, for providing helpful administrative support throughout the project.
Financial Support: This work was supported as a subcontract from the Brigham and Women's Hospital DEcIDE Method Center under contract 290-05-0016I from the Agency for Healthcare Research and Quality, U.S. Department of Health and Human Services, as part of the Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) program. Dr. Soumerai received grant support from the Centers for Disease Control and Prevention's Natural Experiments for Translation in Diabetes.
Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M13-1887.
Requests for Single Reprints: Laura Faden Garabedian, PhD, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02215; e-mail, email@example.com.
Current Author Addresses: Drs. Garabedian, Toh, and Soumerai: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02215.
Ms. Chu: Harvard PhD Program in Health Policy, 14 Story, 4th Floor, Cambridge, MA 02138.
Dr. Zaslavsky: Department of Health Care Policy, Harvard Medical School, 180 Longwood Avenue, Boston, MA 02115.
Author Contributions: Conception and design: L.F. Garabedian, S. Toh, A.M. Zaslavsky, S.B. Soumerai.
Analysis and interpretation of the data: L.F. Garabedian, P. Chu, S. Toh, A.M. Zaslavsky, S.B. Soumerai.
Drafting of the article: L.F. Garabedian.
Critical revision of the article for important intellectual content: L.F. Garabedian, P. Chu, S. Toh, A.M. Zaslavsky, S.B. Soumerai.
Final approval of the article: L.F. Garabedian, P. Chu, S. Toh, A.M. Zaslavsky, S.B. Soumerai.
Statistical expertise: L.F. Garabedian, P. Chu, S. Toh, A.M. Zaslavsky, S.B. Soumerai.
Obtaining of funding: S.B. Soumerai.
Collection and assembly of data: L.F. Garabedian, P. Chu.
Garabedian LF, Chu P, Toh S, Zaslavsky AM, Soumerai SB. Potential Bias of Instrumental Variable Analyses for Observational Comparative Effectiveness Research. Ann Intern Med. 2014;161:131-138. doi: 10.7326/M13-1887
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Published: Ann Intern Med. 2014;161(2):131-138.
Instrumental variable analysis is an increasingly popular method in comparative effectiveness research (CER). In theory, the instrument controls for unobserved and observed patient characteristics that affect the outcome. However, the results of instrumental variable analyses in observational settings may be biased if the instrument and outcome are related through an unadjusted third variable: an “instrument–outcome confounder.”
The authors identified published CER studies that used instrumental variable analysis and searched the literature for potential confounders of the most common instrument–outcome pairs. Of the 187 studies identified, 114 used 1 or more of the 4 most common instrument categories: distance to facility, regional variation, facility variation, and physician variation. Of these, 65 used mortality as an outcome. Potential unadjusted instrument–outcome confounders were observed in all studies, including patient race, socioeconomic status, clinical risk factors, health status, and urban or rural residency; facility and procedure volume; and co-occurring treatments. Only 4 (6%) instrumental variable CER studies considered potential instrument–outcome confounders outside the study data. Many effect estimates may be biased by the failure to adjust for instrument–outcome confounding. The authors caution against overreliance on instrumental variable studies for CER.
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