Use of instrumental variable in prescription drug research with observational data: a systematic review
Meyers Primary Care Institute
Medical Subject Headings
Bias (Epidemiology); Confounding Factors (Epidemiology); Observation; Prescription Drugs; Epidemiologic Research Design
Epidemiology | Life Sciences | Medicine and Health Sciences
OBJECTIVE: Instrumental variable (IV) analysis may offer a useful approach to the problem of unmeasured confounding in prescription drug research if the IV is: (1) strongly and unbiasedly associated to treatment assignment; and (2) uncorrelated with factors predicting the outcome (key assumptions).
STUDY DESIGN AND METHODS: We conducted a systematic review of the use of IV methods in prescription drug research to identify the major types of IVs and the evidence for meeting IV assumptions. We searched MEDLINE, OVID, PsychoInfo, EconLit, and economic databases from 1961 to 2009.
RESULTS: We identified 26 studies. Most (n=16) were published after 2007. We identified five types of IVs: regional variation (n=8), facility-prescribing patterns (n=5), physician preference (n=8), patient history/financial status (n=3), and calendar time (n=4). Evidence supporting the validity of IV was inconsistent. All studies addressed the first IV assumption; however, there was no standard for demonstrating that the IV sufficiently predicted treatment assignment. For the second assumption, 23 studies provided explicit argument that IV was uncorrelated with the outcome, and 16 supported argument with empirical evidence.
CONCLUSIONS: Use of IV methods is increasing in prescription drug research. However, we did not find evidence of a dominant IV. Future research should develop standards for reporting the validity and strength of IV according to key assumptions.
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Citation: J Clin Epidemiol. 2011 Jun;64(6):687-700. Epub 2010 Dec 16. DOI 10.1016/j.jclinepi.2010.09.006
DOI of Published Version
Journal of clinical epidemiology
Chen, Yong and Briesacher, Becky A., "Use of instrumental variable in prescription drug research with observational data: a systematic review" (2011). GSBS Student Publications. 1667.