Journal
JOURNAL OF CLINICAL EPIDEMIOLOGY
Volume 62, Issue 12, Pages 1226-1232Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.jclinepi.2008.12.005
Keywords
Pharmacoepidemiology; Instrumental variable; Confounding factor (epidemiology); Bias (epidemiology); Physician prescribing preference; Unmeasured confounding
Funding
- National Institute on Aging [RO1-AG021950]
- National Institute of Mental Health [U01-MH078708]
- Agency for Healthcare Research and Quality [AHRQ
- 2-RO1-HS10881]
- Department of Health and Human Services, Rockville, MD
- AHRQ
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The gold standard of study design for treatment evaluation is widely acknowledged to be the randomized controlled trial (RCT). Trials allow for the estimation of causal effect by randomly assigning participants either to an intervention or comparison group; through the assumption of exchangeability between groups, comparing the outcomes will yield an estimate of causal effect. In the many cases where RCTs are impractical or unethical, instrumental variable (IV) analysis offers a nonexperimental alternative based on many of the same principles. IV analysis relies on finding a naturally varying phenomenon, related to treatment but not to outcome except through the effect of treatment itself, and then using this phenomenon as a proxy for the confounded treatment variable. This article demonstrates how IV analysis arises from an analogous but potentially impossible RCT design, and outlines the assumptions necessary for valid estimation. It gives examples of instruments used in clinical epidemiology and concludes with an outline on estimation of effects. (C) 2009 Elsevier Inc. All rights reserved.
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