Cited by 465Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyMeta-analysis and systematic reviews

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Instrumental Variables: Application and Limitations

Martens, Edwin P., Pestman, Wiebe R., de Boer, Anthonius, Belitser, Svetlana V., Klungel, Olaf H.·2006·Epidemiology

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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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de Wit, L. A., Croll, D. A., Tershy, B. R., Newton, K., Spatz, D. R., Holmes, N. D., & Kilpatrick, A. M. (2017) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.16-0573 · Cited by 23
“causation between disease data and the socioeconomic variables, which would indicate a need to add instrumental variables to our models.(54) For all models, we did not find any correlation between residuals and the socioeconomic”
Method
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Michalek, J. E., Kepa, A., Vincent, J. B., Frissa, S., Goodwin, L., Hotopf, M. H., Hatch, S. L., Breen, G. D., & Powell, T. R. (2017) · Journal of Affective Disorders · doi:10.1016/j.jad.2017.01.017 · Cited by 25
“Nonetheless, it was previously argued that the second and third assumptions are not possible to describe empirically (Martens et al., 2006).”
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Kino, S., Sato, K., & Kawachi, I. I. (2018) · International Journal for Equity in Health · doi:10.1186/s12939-018-0877-y · Cited by 23
Instantaneous preference was a stronger instrumental variable than 3- and 6-month prescribing preference for NSAIDs
Hennessy, S., Leonard, C. E., Palumbo, C. M., Shi, X., & Have, T. R. T. (2008) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2008.01.003 · Cited by 23
“The IV approach is also known as estimation of simultaneous regression equations and two-stage least squares regression (2).”
BackgroundMethodInfluential
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Ormel, J., Spinhoven, P., de Vries, Y. A., Cramer, A. O. J., Siegle, G. J., Bockting, C. L. H., & Hollon, S. D. (2020) · Psychological Medicine · doi:10.1017/s0033291719003295 · Cited by 22
“The methods are instrumental variable analysis (Martens, Pestman, de Boer, Belitser, & Klungel, 2006) and propensity score analysis (Austin, 2011).”
Cigarette Smoking and Endometrial Cancer Risk: Observational and Mendelian Randomization Analyses
Dimou, N. L., Omiyale, W., Biessy, C., Viallon, V., Kaaks, R., O’Mara, T. A., Aglago, E. K., Ardanáz, E., Bergmann, M. M., Bondonno, N. P., et al. (2022) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-21-1176 · Cited by 23
“The MR-Egger regression (27) and the estimator from the weighted median approach (28) were used to investigate the potential violation of the MR assumptions (29, 30) and to account for potential horizontal pleiotropic effects on the causal estimates.”
Method
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“11 Hence, associations between genetic predictors of BMI and mortality provide insights into the causal effect of BMI on mortality.”
Background
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Li, L., KLEINMAN, K. P., & Gillman, M. W. (2014) · Journal of Developmental Origins of Health and Disease · doi:10.1017/s2040174414000415 · Cited by 21
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Schmoor, C., Gall, C., Stampf, S., & Graf, E. (2011) · Biometrical journal. Biometrische Zeitschrift · doi:10.1002/bimj.201000154 · Cited by 23
“They concentrate on PS approaches (D’Agostino, 1998; Imbens, 2004), on IV approaches (Martens et al., 2006; Hernan and Robins, 2006; Rassen et al., 2009; Brookhart et al., 2010), or comprise both approaches (Klungel et al., 2004).”
Background
A selective review of the first 20 years of instrumental variables models in health-services research and medicine
Cawley, J. (2015) · Journal of Medical Economics · doi:10.3111/13696998.2015.1043917 · Cited by 19
“This section provides the basic intuition to the model of IV; readers seeking additional information are referred to detailed guides such as Wooldridge(6), Angrist and Pischke(4), and Martens et al.(7).”
Background
An approach for estimating the health effects of changes over time in air pollution: an illustration using cardio-respiratory hospital admissions in London
Tonne, C. C., Beevers, S. D., Kelly, F., Järup, L., Wilkinson, P., & Armstrong, B. (2009) · Occupational and Environmental Medicine · doi:10.1136/oem.2009.048702 · Cited by 20
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Konetzka, R. T., Yang, F., & Werner, R. M. (2019) · Health Economics · doi:10.1002/hec.3861 · Cited by 20
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Lack of Identification in Semiparametric Instrumental Variable Models With Binary Outcomes
Burgess, S., Granell, R., Palmer, T., Sterne, J. A. C., & Didelez, V. (2014) · American Journal of Epidemiology · doi:10.1093/aje/kwu107 · Cited by 18
OpinionIdentifying Best Practices and Outcomes in the DOPPS: Caring for Dialysis Patients: International Insights from the Dialysis Outcomes and Practice Patterns Study (DOPPS)
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“There is an upper bound on how strong an IV can be that depends on the strength of unmeasured confounding [31].”
Background
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Freue, G. V. C., Ortiz‐Molina, H., & Zamar, R. H. (2013) · Biometrics · doi:10.1111/biom.12043 · Cited by 18
“…studies based on observational data often use instrumental variables estimators to address endogeneity problems (see the surveys of Greenland, 2000, Martens et al., 2006, Grootendorst, 2007, and the article of McClellan and Newhouse, 2000 which introduces a special issue of Health Services…”
Background
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Groenwold, R. H. H., Hak, E., Klungel, O. H., & Hoes, A. W. (2009) · Value in Health · doi:10.1111/j.1524-4733.2009.00584.x · Cited by 18
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Self-citation
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“The F statistic is considered as a metric for the strength of the instrument, with higher values signifying a better instrument [10, 11, 37, 41].”

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.
ORCID: Belitser, Svetlana V.
Klungel, Olaf H.
Venue: Epidemiology
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2006-05
Volume: 17
Issue: 3
Pages: 260-267
ISSN: 1044-3983 (ISSN-L)
DOI: 10.1097/01.ede.0000215160.88317.cb
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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Instrumental Variables: Application and Limitations

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