Use of instrumental variable in prescription drug research with observational data: a systematic review
dc.contributor.author | Chen, Yong | |
dc.contributor.author | Briesacher, Becky A. | |
dc.date | 2022-08-11T08:08:54.000 | |
dc.date.accessioned | 2022-08-23T16:11:16Z | |
dc.date.available | 2022-08-23T16:11:16Z | |
dc.date.issued | 2011-06-14 | |
dc.date.submitted | 2010-12-20 | |
dc.identifier.citation | J Clin Epidemiol. 2011 Jun;64(6):687-700. Epub 2010 Dec 16. DOI 10.1016/j.jclinepi.2010.09.006 | |
dc.identifier.issn | 1878-5921 | |
dc.identifier.doi | 10.1016/j.jclinepi.2010.09.006 | |
dc.identifier.pmid | 21163621 | |
dc.identifier.uri | http://hdl.handle.net/20.500.14038/33125 | |
dc.description.abstract | 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. | |
dc.language.iso | en_US | |
dc.publisher | Elsevier | |
dc.relation | <a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&list_uids=21163621&dopt=Abstract">Link to article in PubMed</a> | |
dc.relation.url | http://dx.doi.org/10.1016/j.jclinepi.2010.09.006 | |
dc.subject | Bias (Epidemiology); Confounding Factors (Epidemiology); Observation; Prescription Drugs; Epidemiologic Research Design | |
dc.subject | Epidemiology | |
dc.subject | Life Sciences | |
dc.subject | Medicine and Health Sciences | |
dc.title | Use of instrumental variable in prescription drug research with observational data: a systematic review | |
dc.type | Journal Article | |
dc.source.journaltitle | Journal of clinical epidemiology | |
dc.identifier.legacycoverpage | https://escholarship.umassmed.edu/gsbs_sp/1667 | |
dc.identifier.contextkey | 1703063 | |
html.description.abstract | <p>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).</p> <p>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.</p> <p>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.</p> <p>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.</p> | |
dc.identifier.submissionpath | gsbs_sp/1667 | |
dc.contributor.department | Meyers Primary Care Institute | |
dc.contributor.student | Yong Chen |