The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Instrumental Variables: Application and Limitations
Abstract
To correct for confounding, the method of instrumental variables (IV) has been proposed. Its use in medical literature is still rather limited because of unfamiliarity or inapplicability. By introducing the method in a nontechnical way, we show that IV in a linear model is quite easy to understand and easy to apply once an appropriate instrumental variable has been identified. We also point out some limitations of the IV estimator when the instrumental variable is only weakly correlated with the exposure. The IV estimator will be imprecise (large standard error), biased when sample size is small, and biased in large samples when one of the assumptions is only slightly violated. For these reasons, it is advised to use an IV that is strongly correlated with exposure. However, we further show that under the assumptions required for the validity of the method, this correlation between IV and exposure is limited. Its maximum is low when confounding is strong, such as in case of confounding by indication. Finally, we show that in a study in which strong confounding is to be expected and an IV has been used that is moderately or strongly related to exposure, it is likely that the assumptions of IV are violated, resulting in a biased effect estimate. We conclude that instrumental variables can be useful in case of moderate confounding but are less useful when strong confounding exists, because strong instruments cannot be found and assumptions will be easily violated.
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“IV analyses are an alternative estimation method that can be used to derive an estimate of the causal effect of the treatment (here, CRP) on the outcome (SBP) by accounting for the size of the association between randomization and CRP.3,4,12–15”
From Mendelian randomization (2022) · cites this paper as [13]
Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. H. (1997) · Econometrica · doi:10.2307/2171753 · Cited by 7,081
Instrumental Variables Regression with Weak Instruments
Staiger, D., & Stock, J. (1994) · doi:10.3386/t0151 · Cited by 5,302 · cited 4×
“However, it has been argued that the application of this method is limited because of its strong assumptions, making it difficult in practice to find a suitable instrumental variable.(16) The objectives of this article are first to introduce the application of the method of IV in epidemiology in a nontechnical way and…”
BackgroundMethodInfluentialIdentification 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
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(1947) · The American Statistician · doi:10.1080/00031305.1947.10501534 · Cited by 3,745
Randomized, Controlled Trials, Observational Studies, and the Hierarchy of Research Designs
Concato, J., Shah, N., & Horwitz, R. I. (2000) · New England Journal of Medicine · doi:10.1056/nejm200006223422507 · Cited by 3,563
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Research works printed but not linked: 1
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Sorted by citations
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922
“The following three assumptions are necessary for a genetic variant to be a valid IV (Martens et al., 2006): IV1: the variant is predictive of the exposure; IV2: the variant is independent of any confounding factors of the exposure—outcome association; IV3: the variant is conditionally independent…”
BackgroundInfluentialAvoiding bias from weak instruments in Mendelian randomization studies
Burgess, S., Thompson, S. G., & CRP CHD Genetics Collaboration (2011) · International Journal of Epidemiology · doi:10.1093/ije/dyr036 · Cited by 3,952
Roux, A. V. D., & Mair, C. (2010) · Annals of the New York Academy of Sciences · doi:10.1111/j.1749-6632.2009.05333.x · Cited by 2,922
Mendelian Randomization as an Approach to Assess Causality Using Observational Data
Sekula, P., Del Greco M, F., Pattaro, C., & Köttgen, A. (2016) · Journal of the American Society of Nephrology · doi:10.1681/asn.2016010098 · Cited by 2,351
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
Metadata
- Authors
- Martens, E. P., Pestman, W. R., de Boer, A., Belitser, S. V., & Klungel, O. H.
- Affiliations
- Utrecht University (NL)
- Corresponding author
- Martens, Edwin P.
- Belitser, Svetlana V.
- Klungel, Olaf H.
- Venue
- Epidemiology
- Published
- 2006-05
- Volume
- 17
- Issue
- 3
- Pages
- 260-267
- ISSN
- 1044-3983 (ISSN-L)
- OpenAlex
- W2127065386
- Cited by
- 464
- References
- 37
- Citations by year
- 2026: 19 · 2025: 35 · 2024: 31 · 2023: 29 · 2022: 35 · 2021: 29 · 2020: 26 · 2019: 26 · 2018: 21 · 2017: 22 · 2016: 18 · 2015: 28 · 2014: 24 · 2013: 22 · 2012: 27
- FWCI
- 10.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Epidemiology (publishedVersion)
- PubMed (publishedVersion)
- Utrecht University Repository (Utrecht University) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- instrumental variables, confounding, confounding by indication, weak instruments, linear regression, exposure measurement error
- MeSH terms
- United States, Models, Statistical, Bias, Confounding Factors, Epidemiologic, Epidemiologic Studies
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