The DOI matches its Crossref record; 71 of the 71 references it cites were found in a registry or an index.
Instrumental variable methods in comparative safety and effectiveness research
Open accessAbstract
Instrumental variable (IV) methods have been proposed as a potential approach to the common problem of uncontrolled confounding in comparative studies of medical interventions, but IV methods are unfamiliar to many researchers. The goal of this article is to provide a non‐technical, practical introduction to IV methods for comparative safety and effectiveness research. We outline the principles and basic assumptions necessary for valid IV estimation, discuss how to interpret the results of an IV study, provide a review of instruments that have been used in comparative effectiveness research, and suggest some minimal reporting standards for an IV analysis. Finally, we offer our perspective of the role of IV estimation vis‐à‐vis more traditional approaches based on statistical modeling of the exposure or outcome. We anticipate that IV methods will be often underpowered for drug safety studies of very rare outcomes, but may be potentially useful in studies of intended effects where uncontrolled confounding may be substantial. Copyright © 2010 John Wiley & Sons, Ltd.
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Citations
Cites
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Hausman, J. A. (1978) · Econometrica · doi:10.2307/1913827 · Cited by 18,820
Instrumental Variables Regression with Weak Instruments
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Staiger, D., & Stock, J. (1994) · doi:10.3386/t0151 · Cited by 5,302 · cited 2×
“First, standard IV estimators possess a finite sample bias which is inversely proportional to the F statistic that tests whether the included instruments make a significant contribution to the first-stage model.(10,11) Therefore, adding weak instruments to the first stage can increase bias.”
BackgroundIdentification of Causal Effects Using Instrumental Variables
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Identification and estimation of local average treatment effects
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Identification and Estimation of Local Average Treatment Effects
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Instrumental Variables and GMM: Estimation and Testing
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Variable Selection for Propensity Score Models
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Cited by
Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229
Skrivankova, V. W., Richmond, R. C., Woolf, B. A. R., Davies, N. M., Swanson, S. A., VanderWeele, T. J., Timpson, N. J., Higgins, J. P. T., Dimou, N., Langenberg, C., et al. (2021) · BMJ · doi:10.1136/bmj.n2233 · Cited by 1,648
Registries for Evaluating Patient Outcomes: A User’s Guide
(2020) · doi:10.23970/ahrqepcregistries4 · Cited by 960
Craig, P., Cooper, C., Gunnell, D., Haw, S., Lawson, K., Macintyre, S., Ogilvie, D., Petticrew, M., Reeves, B., Sutton, M., et al. (2012) · Journal of Epidemiology and Community Health · doi:10.1136/jech-2011-200375 · Cited by 912
Commentary: How to Report Instrumental Variable Analyses (Suggestions Welcome)
Swanson, S. A., & Hernán, M. A. (2013) · Epidemiology · doi:10.1097/ede.0b013e31828d0590 · Cited by 197
Metadata
- Authors
- Brookhart, M. A., Rassen, J. A., & Schneeweiss, S.
- Venue
- Pharmacoepidemiology and Drug Safety
- Publisher
- Wiley
- Published
- 2010-06
- Volume
- 19
- Issue
- 6
- Pages
- 537-554
- ISSN
- 1053-8569 (ISSN-L)
- 1099-1557
- OpenAlex
- W2169711166
- Cited by
- 332
- References
- 71
- FWCI
- 18.58
- Type
- journal article
- Language
- en
- Open access
- green
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