The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Evaluating Short-Term Drug Effects Using a Physician-Specific Prescribing Preference as an Instrumental Variable
Open accessAbstract
BACKGROUND: Postmarketing observational studies of the safety and effectiveness of prescription medications are critically important but fraught with methodological problems. The data sources available for such research often lack information on indications and other important confounders for the drug exposure under study. Instrumental variable methods have been proposed as a potential approach to control confounding by indication in nonexperimental studies of treatment effects; however, good instruments are hard to find. METHODS: We propose an instrument for use in pharmacoepidemiology that is based on a time-varying estimate of the prescribing physician's preference for one drug relative to a competing therapy. The use of this instrument is illustrated in a study comparing the effect of exposure to COX-2 inhibitors with nonselective, nonsteroidal antiinflammatory medications on gastrointestinal complications. RESULTS: Using conventional multivariable regression adjusting for 17 potential confounders, we found no protective effect due to COX-2 use within 120 days from the initial exposure (risk difference = -0.06 per 100 patients; 95% confidence interval = -0.26 to 0.14). However, the proposed instrumental variable method attributed a protective effect to COX-2 exposure (-1.31 per 100 patients; -2.42 to -0.20) compatible with randomized trial results (-0.65 per 100 patients; -1.08 to -0.22). CONCLUSIONS: The instrumental variable method that we have proposed appears to have substantially reduced the bias due to unobserved confounding. However, more work needs to be done to understand the sensitivity of this approach to possible violations of the instrumental variable assumptions.
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Cites
Estimating causal effects of treatments in randomized and nonrandomized studies
Rubin, D. B. (1974) · Journal of Educational Psychology · doi:10.1037/h0037350 · Cited by 9,436
Identification of Causal Effects Using Instrumental Variables
Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996) · Journal of the American Statistical Association · doi:10.1080/01621459.1996.10476902 · Cited by 4,230
An introduction to instrumental variables for epidemiologists
Greenland, S. (2000) · International Journal of Epidemiology · doi:10.1093/ije/29.4.722 · Cited by 1,156
When are observational studies as credible as randomised trials?
Vandenbroucke, J. P. (2004) · The Lancet · doi:10.1016/s0140-6736(04)16261-2 · Cited by 538
Cited by
Negative Controls: A Tool for Detecting Confounding and Bias in Observational Studies
Lipsitch, M., Tchetgen Tchetgen, E., & Cohen, T. (2010) · Epidemiology · doi:10.1097/ede.0b013e3181d61eeb · Cited by 1,583
Instruments for Causal Inference: An Epidemiologist's Dream?
Hernán, M. A., & Robins, J. M. (2006) · Epidemiology · doi:10.1097/01.ede.0000222409.00878.37 · Cited by 961
Risk of Death in Elderly Users of Conventional vs. Atypical Antipsychotic Medications
Wang, P. S., Schneeweiss, S., Avorn, J., Fischer, M. A., Mogun, H., Solomon, D. H., & Brookhart, M. A. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa052827 · Cited by 959
Self-citationSchneeweiss, S., Setoguchi, S., Brookhart, A., Dormuth, C., & Wang, P. S. (2007) · Canadian Medical Association Journal · doi:10.1503/cmaj.061250 · Cited by 366
Self-citationWhen and how can real world data analyses substitute for randomized controlled trials?
Franklin, J. M., & Schneeweiss, S. (2017) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.857 · Cited by 316
Metadata
- Authors
- Brookhart, M. A., Wang, P. S., Solomon, D. H., & Schneeweiss, S.
- Venue
- Epidemiology
- Published
- 2006-05
- Volume
- 17
- Issue
- 3
- Pages
- 268-275
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2142195373
- Cited by
- 341
- References
- 4
- FWCI
- 15.86
- Type
- journal article
- Language
- en
- Open access
- green
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