Cited by 198Advanced Causal Inference TechniquesMeta-analysis and systematic reviewsStatistical Methods in Epidemiology

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Commentary: How to Report Instrumental Variable Analyses (Suggestions Welcome)

Swanson, Sonja A., Hernán, Miguel A.·2013·Epidemiology

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Abstract

Instrumental variable (IV) methods are becoming mainstream in comparative effectiveness research,1 but IV methods are radically different from traditional epidemiologic methods. The goal of IV methods is to eliminate confounding without ever measuring the confounders. This apparent miracle can be achieved only when four conditions are met (see below).2,3 Here we suggest a checklist for investigators who use IV methods. Like others before,4 we hope this step-by-step guide will improve the reporting of IV estimates and increase the transparency of the underlying assumptions. Our discussion focuses on reports of causal effects of medical interventions and is informed by two papers by Davies et al5,6 that appear in this issue of EPIDEMIOLOGY: an application that estimates the causal effects of cyclooxygenase-2 (COX-2) selective versus nonselective nonsteroidal anti-inflammatory drugs (NSAIDs), and a literature review of IV papers that describes their use, and perhaps misuse, in epidemiology. We supplement this review with additional information from our own review of IV analyses of observational studies with a relatively well-defined medical intervention. Details of our review can be found in the online supplement (https://links.lww.com/EDE/A664). IV methods require a variable—the “instrument”—that meets the three so-called instrumental conditions: (1) the instrument is associated with the treatment, (2) the instrument does not affect the outcome except through treatment (also known as the exclusion restriction assumption), and (3) the instrument does not share any causes with the outcome. An example of such an instrument is the randomization indicator in double-blind randomized experiments.2 Davies et al6 summarize instruments that have been proposed in epidemiologic studies, including the physician preference type they use themselves.5 Unfortunately, no variable can be proved to be an instrument in observational studies because only condition (1) can be empirically verified. We outline some steps for reporting IV analyses based on the variables proposed as instruments (Figure) and discuss how these steps have been reported in previous studies (Table). A detailed specification of how IV methods should be implemented is beyond the scope of this commentary.FIGURE: Flowchart for reporting of IV analyses with a single, non–time-varying, dichotomous instrument.TABLE: Proportions Reporting According to Suggested Guidelines in a Review of IV Publications Assessing Effects of Medical Interventions (n = 81)The first step in our reporting flowchart is to empirically verify condition (1). When the association between the proposed instrument and the treatment is weak, the proposed “weak” instrument may amplify biases due to small violations of conditions (2) or (3), producing very biased effect estimates.7,8 Alternatively, if the proposed instrument is very strong, it may be more likely to violate conditions (2) or (3); in the extreme, a perfect correlation between the proposed instrument and the observational treatment implies the proposed instrument is associated with the same set of (possibly unmeasured) confounders as the treatment.9 Most prior studies have evaluated the strength of their proposed instrument via risk differences, odds ratios, or partial R2 or F statistics like Davies et al5 did and several authors have recommended.6,8 (In contrast, the use of R2 or F statistics as effect measures is subject to many study-specific artifacts and should generally be avoided.10,11) Unlike condition (1), conditions (2) and (3) are not empirically verifiable: they are assumptions. Therefore, the next step is to use subject-matter knowledge to build a case for why the proposed instrument may be reasonably assumed to meet each of these two conditions; this is similar to how one would use subject-matter knowledge to justify the no unmeasured confounding assumption of traditional methods. When dichotomizing a polytomous or continuous treatment, condition (2) may hold for the original treatment, but not for its dichotomous version. Investigators can provide some support for condition (3) by showing the proposed instrument is not associated with measured confounders.6 Although any associations found with measured covariates could be adjusted for by modeling (see below), associations with measured confounders may raise concerns over associations with unmeasured confounders. Davies et al5 provide arguments to support conditions (2) and (3) when physician’s preference is proposed as an instrument; other authors have provided counterarguments.2 In our review, we notice a tendency for conditions (2) and (3) to be treated as a single condition. Although these conditions are statistically similar, it is important to consider them separately in order to incorporate subject-matter knowledge in discussions about their validity and in decisions on adjustment. For example, Davies et al5 found some evidence that the proposed instrument is ass

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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
Identification and estimation of local average treatment effects
Imbens, G. W., & Angrist, J. D. (1994) · Econometrica · doi:10.2307/2951620 · Cited by 4,101
Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogeneous Explanatory Variable is Weak
Bound, J., Jaeger, D. A., & Baker, R. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476536 · Cited by 3,898
Causal Diagrams for Epidemiologic Research
Greenland, S., Pearl, J., & Robins, J. M. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00008 · Cited by 3,728
Air Pollution and Hospital Admissions for Heart Disease in Eight U.S. Counties
Schwartz, J. D. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00005 · Cited by 3,045
Identification and Estimation of Local Average Treatment Effects
Angrist, J. D., & Imbens, G. W. (1995) · National Bureau of Economic Research · doi:10.3386/t0118 · Cited by 2,915
Maternal Pesticide Exposure from Multiple Sources and Selected Congenital Anomalies
Shaw, G. M., Wasserman, C. R., O’Malley, C. D., Nelson, V. E., & Jackson, R. J. (1999) · Epidemiology · doi:10.1097/00001648-199901000-00011 · Cited by 1,783
Causal knowledge as a prerequisite for confounding evaluation: an application to birth defects epidemiology
Hernan, M. A. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.2.176 · Cited by 1,445
Quantifying Biases in Causal Models: Classical Confounding vs Collider-Stratification Bias
Greenland, S. (2003) · Epidemiology · doi:10.1097/01.ede.0000042804.12056.6c · Cited by 810
Bounds on Treatment Effects from Studies with Imperfect Compliance
Balke, A., & Pearl, J. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10474074 · Cited by 564

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Kurz, M., Tatangelo, M., Morin, K. A., Zanette, M., Krebs, E., Marsh, D. C., & Nosyk, B. (2025) · PLoS ONE · doi:10.1371/journal.pone.0322064 · Cited by 1
“Furthermore, measures of prescribing preferences are commonly used in comparative effectiveness studies using instrumental variable analysis to address unmeasured confounding by indication [48–52].”
Evaluation of amyloid change as a surrogate for cognitive decline: demonstration in individual‐level data from the A4 study of solanezumab
Ackley, S. F., Flanders, M. D., Murchland, A. R., Chen, R., Wang, J., Shah, S., Huey, E. D., & Glymour, M. M. (2026) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.70205 · Cited by 1
“29 Further discussion of mediation and IV can be found in the Supplemental methods.”
Concordance with known causal effects is a potential validity measure for observational studies
Schooling, C. M. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.01.016 · Cited by 1
Comments on the justification of the independence assumption in ‘Does cycle commuting reduce the risk of mental ill-health? An instrumental variable analysis using distance to nearest cycle path’
Hartwig, F. P., & Smith, G. D. (2024) · International Journal of Epidemiology · doi:10.1093/ije/dyae094 · Cited by 1
How Do Information and Communication Technologies (ICTs) Affect National Well-Being? The Contrasting Influence of Mobile Phones and Household Computers
Dev, M., Kumar, M., & Saha, D. (2025) · Social Indicators Research · doi:10.1007/s11205-025-03671-1 · Cited by 1
“We performed a two-stage least square (2SLS) regression to test for potential endogeneity concerns, such as omitted variable bias and reverse causality (Swanson & Hernán, 2013).”
Quantifying and Reducing Uncertainty about Causality in Improving Public Health and Safety
Cox, L. A. (2015) · Springer eBooks · doi:10.1007/978-3-319-11259-6_71-1 · Cited by 1
Reporting of Baseline Characteristics to Accompany Analysis by Instrumental Variables
MacKenzie, T. A., O’Malley, A. J., & Bekelis, K. (2018) · Epidemiology · doi:10.1097/ede.0000000000000914 · Cited by 1
Detecting Heterogeneous Treatment Effect with Instrumental VariablesPreprint
Johnson, M. L., Cao, J., & Kang, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.03652 · Cited by 1
“In this paper, we focus on studying the CATE; see Imbens (2010) and Swanson and Hernán (2013, 2014) for discussions on CATE.”
Vaccine Effectiveness Against Hospitalization Among Adolescent and Pediatric SARS-CoV-2 Cases in Ontario, CanadaPreprint
Simmons, A. E., Amoako, A., Grima, A. A., Murison, K. R., Buchan, S. A., Tuite, A. R., & Fisman, D. N. (2022) · medRxiv · doi:10.1101/2022.03.24.22272919 · Cited by 1
“, no effect measure modification) (25).”
Examining the evidence for Mendelian randomization homogeneity assumption violation using instrument association with exposure variancePreprint
Lyon, M., Millard, L. A. C., Smith, G. D., Hartwig, F. P., Gaunt, T. R., & Tilling, K. M. (2022) · medRxiv · doi:10.1101/2022.09.12.22279854 · Cited by 1
Causal models
Loh, W. W., & Kim, J. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-818630-5.10078-8 · Cited by 2
Instrumental Variables and Mendelian Randomization With Invalid Instruments
Kang, H. (2015) · Scholarly Commons (University of Pennsylvania)
Toward an Understanding of the Challenges and Opportunities when Studying Emerging Therapies
Mack, C. D., & Gokhale, M. (2016) · Current Epidemiology Reports · doi:10.1007/s40471-016-0090-8
Cardiovascular Effects Of Incretin-Based Antihyperglycemic Drugs Relative To Treatment Alternatives In Older Adults
Gokhale, M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/athz-m068
Lessons from Mendelian randomization studies on liver biomarkers: response to Abbasi
Noordam, R., Smit, R. A. J., Postmus, I., Trompet, S., & van Heemst, D. (2017) · International Journal of Epidemiology · doi:10.1093/ije/dyx064
Antibiotic Prescribing during Infancy and Risk of Treated Obstructive Airway Diseases during Early Childhood: A Registry-Based Nationwide Cohort Study in Denmark
Kinlaw, A. C. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/4hxh-p784
Approach for Genetic Studies
Ken‐Dror, G., & Sharma, P. (2021) · Springer eBooks · doi:10.1007/978-3-030-70761-3_13
Intraoperative Hydromorphone Decreases Post-Operative Pain Who Would Have Thought? An Instrumental Variable AnalysisPreprint
Ershoff, B. D. (2021) · medRxiv · doi:10.1101/2021.10.18.21263855
“[30] The monotonicity assumption states that the exposure is a nondecreasing function (or a non-increasing function) of the IV for all persons.”
Background
Additional file 1 of Instrumental variables for implementation science: exploring context-dependent causal pathways between implementation strategies and evidence-based interventions
Mody, A., Filiatreau, L. M., Goss, C. W., Powell, B. J., & Geng, E. (2023) · Figshare · doi:10.6084/m9.figshare.24883601
Use of the instrumental inequalities in simulated mendelian randomization analyses with coarsened exposures
Diemer, E. W., Shi, J., Hernán, M. Á., & Swanson, S. A. (2024) · European Journal of Epidemiology · doi:10.1007/s10654-024-01130-8
“Investigators should use the instrumental inequalities in concert with other falsi-fication approaches and consider the best approach for their analysis based on subject matter knowledge of the topic area, as outlined in previous guidelines [15, 21].”
Self-citation
Pharmacoepidemiology in Cardiovascular Research
Ko, D., Yoo, T. K., & Kim, D. H. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15734-9.00031-5
Towards scalable biomarker discovery in posttraumatic stress disorder: triangulating genomic and phenotypic evidence from a health system biobank
Lee, Y. A., Zhang, Y., Dice, A. L. E., Li, J. H., Tubbs, J. D., Feng, Y. A., Ge, T., Maihofer, A. X., Nievergelt, C. M., Smoller, J. W., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03553-z · Cited by 1
The impact of biosimilar use on healthcare utilization among new users of etanercept for inflammatory arthritis: a population-based regression discontinuity analysis
Zhou, V. Y., Lacaille, D. V., Zheng, Y., Qian, Y., Nosyk, B., & Xie, H. W. (2025) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2025.101058
“38 Instead, they can be supported by subject knowledge and/or falsi ﬁ ed through extensive testing.”
Statistical Methods for Mendelian Randomization
Orihara, S., & Hirano, K. (2025) · Japanese Journal of Biometrics · doi:10.5691/jjb.46.1
Heterogeneity of treatment preferences in the absence of guideline recommendations – a case vignette study in colorectal cancer tumor boards in Germany, Austria and Switzerland
Soff, J., Benz, S. R., Kowalski, C., Hansinger, J., Gerken, M., Maier, D., Kim-Wanner, S.-Z., Witzmann, J., Reinacher‐Schick, A., Pox, C. P., et al. (2025) · BMC Gastroenterology · doi:10.1186/s12876-025-04183-5
“Institution-specific treatment preferences can serve as a basis to address confounding-by-indication in observational data, when they are associated with the treatment, are independent from the outcome and do not affect it other than through the treatment [41, 42].”
Additional file 1 of Instrumental variable analysis to estimate treatment effects: a simulation study showing potential benefits of conditioning on hospital
Ceyisakar, I. E., van Leeuwen, N., Steyerberg, E. W., & Lingsma, H. F. (2022) · Figshare · doi:10.6084/m9.figshare.19654233
Additional file 1 of Exploring the causal effect of maternal pregnancy adiposity on offspring adiposity: Mendelian randomisation using polygenic risk scores
Bond, T. A., Richmond, R. C., Karhunen, V., Cuéllar-Partida, G., Borges, M. C., Zuber, V., Couto Alves, A., Mason, D., Yang, T. C., Gunter, M. J., et al. (2022) · Figshare · doi:10.6084/m9.figshare.19101987
Additional file 1 of Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines
Hufstedler, H., Mauer, N. S., Yeboah, E., Carr, S., Rahman, S., Danzer, A. M., Debray, T. P. A., de Jong, V. M. T., Campbell, H., Gustafson, P., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26708882
Additional file 2 of Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines
Hufstedler, H., Mauer, N. S., Yeboah, E., Carr, S., Rahman, S., Danzer, A. M., Debray, T. P. A., de Jong, V. M. T., Campbell, H., Gustafson, P., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26708885
Additional file 1 of Reporting methodological issues of the mendelian randomization studies in health and medical research: a systematic review
Islam, S. N., Ahammed, T., Anjum, A., Albalawi, O., & Uddin, M. J. (2022) · Figshare · doi:10.6084/m9.figshare.18517702
Additional file 6 of Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines
Hufstedler, H., Mauer, N. S., Yeboah, E., Carr, S., Rahman, S., Danzer, A. M., Debray, T. P. A., de Jong, V. M. T., Campbell, H., Gustafson, P., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26708897
Additional file 2 of Instrumental variable analysis to estimate treatment effects: a simulation study showing potential benefits of conditioning on hospital
Ceyisakar, I. E., van Leeuwen, N., Steyerberg, E. W., & Lingsma, H. F. (2022) · Figshare · doi:10.6084/m9.figshare.19654236
Additional file 5 of Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines
Hufstedler, H., Mauer, N. S., Yeboah, E., Carr, S., Rahman, S., Danzer, A. M., Debray, T. P. A., de Jong, V. M. T., Campbell, H., Gustafson, P., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26708894
Additional file 4 of Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines
Hufstedler, H., Mauer, N. S., Yeboah, E., Carr, S., Rahman, S., Danzer, A. M., Debray, T. P. A., de Jong, V. M. T., Campbell, H., Gustafson, P., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26708891
Additional file 3 of Application of causal inference methods in individual-participant data meta-analyses in medicine: addressing data handling and reporting gaps with new proposed reporting guidelines
Hufstedler, H., Mauer, N. S., Yeboah, E., Carr, S., Rahman, S., Danzer, A. M., Debray, T. P. A., de Jong, V. M. T., Campbell, H., Gustafson, P., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26708888
An approach to estimating how effective and well targeted Extreme Risk Protection Orders have been with respect to suicide prevention
Swanson, S. A., Handy, J., Pear, V. A., Zhang, Y., Studdert, D. M., Kang, H., Diemer, E. W., & Miller, M. (2026) · American Journal of Epidemiology · doi:10.1093/aje/kwag038
“We are going to assume that the pre-policy and post-policy populations are comparable with respect to suicide risk and that calendar time is an instrument, 17 , 18 meaning that any time trends in suicide risk across the pre-policy and post-policy eras are entirely due to ERPOs.”
Self-citation
Real‐world comparative effects of slow‐release oral morphine vs. methadone on healthcare use for opioid use disorder: A population‐based target trial emulation
Zhou, V. Y., Fairbairn, N. S., Lei, J., Brar, R., Bach, P., & Socias, M. E. (2026) · Addiction · doi:10.1111/add.70543
Using directed acyclic graphs in observational research: a practical guide for paediatric researchers
Smith, M. J., & Mansournia, M. A. (2026) · Archives of Disease in Childhood · doi:10.1136/archdischild-2026-330279
Exploring the Relationship Between Respiratory Syncytial Virus and Asthma Using Calendar Time at Birth as a Source of Quasi‐Random Variation
Dimitris, M. C., Feikin, D. R., Sparrow, E. G., Schultz, S. E., Kwong, J. C., Buchan, S. A., Savitz, D. A., Brunwasser, S. M., & Fell, D. B. (2026) · Paediatric and Perinatal Epidemiology · doi:10.1111/ppe.70168
Effect of pharmacological treatment of attention-deficit hyperactivity disorder on academic performance: registry-based population study
Lyhmann, I., Widding‐Havneraas, T., Markussen, S., Zachrisson, H. D., Elwert, F., Chaulagain, A., Bjelland, I., Halmøy, A., & Mykletun, A. (2026) · The British Journal of Psychiatry · doi:10.1192/bjp.2026.10754

Authors: Swanson, S. A., & Hernán, M. A.
Affiliations: Harvard University (US)
Corresponding author: Swanson, Sonja A.
ORCID: Swanson, Sonja A.
Venue: Epidemiology
Publisher: Ovid Technologies (Wolters Kluwer Health)
Published: 2013-05
Volume: 24
Issue: 3
Pages: 370-374
ISSN: 1044-3983 (ISSN-L)
DOI: 10.1097/ede.0b013e31828d0590
OpenAlex: W2330290021
Cited by: 197
References: 30
Citations by year: 2026: 8 · 2025: 15 · 2024: 15 · 2023: 12 · 2022: 23 · 2021: 14 · 2020: 9 · 2019: 18 · 2018: 16 · 2017: 15 · 2016: 19 · 2015: 21 · 2014: 10 · 2013: 1
FWCI: 14.82
Type: journal article
Language: en
Open access: closed
Locations: Epidemiology (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Keywords: instrumental variables, reporting guidelines, confounding, exclusion restriction, weak instruments, reporting checklist, observational studies, medical interventions, COX-2 inhibitors, nonsteroidal anti-inflammatory drugs, causal effect estimation
MeSH terms: Data Interpretation, Statistical, Female, Gastrointestinal Diseases, Humans, Male, Myocardial Infarction, Models, Statistical, Epidemiologic Research Design, Causality, Confounding Factors, Epidemiologic, Cyclooxygenase 2 Inhibitors, Research Report

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