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

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

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

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1097/01.ede.0000215160.88317.cb

Cites

37 of 37 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

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…”
BackgroundMethodInfluential
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
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
The American Statistician
(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
Principles of Econometrics
Hannan, E. J., & Theil, H. (1973) · Technometrics · doi:10.2307/1266838 · Cited by 3,238
Analysis of Binary Data
Cox, D. R. (2008) · Springer eBooks · doi:10.1007/978-0-387-32833-1_5 · Cited by 2,687
Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity
Angrist, J. D., & Imbens, G. W. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476535 · Cited by 1,346

Show all 37 references

Research works printed but not linked: 1

Bound, 1995, JASA, 90, 443

Sorted by citations

Items 301–400 of 465

Transesophageal Echocardiography and Outcomes in Coronary Artery Bypass Grafting Surgery: Dealing With Confounders in Observational Studies
Sato, K., & Bainbridge, D. (2019) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2019.11.048 · Cited by 5
Multiplicative versus additive modelling of causal effects using instrumental variables for survival outcomes – a comparison
John, E. R., Crowther, M. J., Didelez, V., & Sheehan, N. A. (2024) · Statistical Methods in Medical Research · doi:10.1177/09622802241293765 · Cited by 4
Causal association between EBV and gastritis, gastric cancer, and gastric ulcer: a two-sample Mendelian randomization study
Qiao, X., Ma, J., Wang, C., Gou, X., & Huo, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-08555-5 · Cited by 4
Impact of environmental regulation on government subsides of public-private partnership waste-to-energy incineration projects: evidence from 66 cities in China
Shen, Y., Xu, M., Cui, C., Xia, B., Skitmore, M., Moorhead, M., & Liu, Y. (2024) · Frontiers in Environmental Science · doi:10.3389/fenvs.2023.1288851 · Cited by 4
Cognitive behavioral program for the prevention of depression in at-risk adolescents: isolating the effects of dose
Dickerson, J. F., Clarke, G. N., Weersing, V. R., Lynch, F. L., Hollon, S. D., Brent, D. A., Beardslee, W. R., Gladstone, T. R. G., Porta, G., DeBar, L., et al. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae131 · Cited by 4
MARKETIZATION AND RESOURCE MISMATCHES: EVIDENCE FROM CHINA’S LAND RESOURCE ALLOCATION
Hu, J., & Du, J. (2024) · The Singapore Economic Review · doi:10.1142/s0217590824470246 · Cited by 4
Instrumental variable analysis in randomized trials with non- compliance and observational pharmacoepidemiologic studies
Groenwold, R. H. H., Uddin, M. J., Roes, K., de Boer, A., Rivero‐Ferrer, E., Martín‐Merino, E., Gatto, N., & Klungel, O. H. (2014) · Cited by 4
Self-citation
The Measure of Financial Stability and its Impact on Foreign Direct Investment: Evidence from BRIC Nations
Kalsie, A., Dhamija, J., & Arora, A. (2020) · International Journal of Accounting & Finance Review · doi:10.46281/ijafr.v5i2.797 · Cited by 4
THE IMPACT OF COLLECTIVISM ORIENTATION AND GENDER IDENTITY ON WORK-LIFE BALANCE AND LIFE SATISFACTION RELATIONSHIP: A UK – SPAIN COMPARISON
Pizarro, I. V., & Arman, G. (2021) · Journal of Management and Business Education · doi:10.35564/jmbe.2021.0004 · Cited by 4
On the selection of variables in criminology: Adjusting for the descendants of unobserved confounders
Silver, I. A., Lonergan, H., & Nedelec, J. L. (2022) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2022.101924 · Cited by 4
Sex differences of the shared genetic landscapes between type 2 diabetes and peripheral artery disease in East Asians and Europeans
Lu, Z., Zhang, H., Yang, Y., & Zhao, H. (2023) · Human Genetics · doi:10.1007/s00439-023-02573-x · Cited by 4
Evaluating the effects of treatment switching with randomization as an instrumental variable in a randomized controlled trial
Jiménez, S., Lai, D., Gould, K. L., & Davis, B. R. (2016) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2016.1140775 · Cited by 4
“That is, the IV should not influence the outcome either directly or indirectly by its relationship with other variables (Martens et al., 2006).”
BackgroundInfluential
Investigating the effect of glycaemic traits on the risk of psychiatric illness using Mendelian randomisationPreprint
Adams, D. M., Reay, W. R., Geaghan, M. P., & Cairns, M. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.09.984690 · Cited by 4
“There are three main assumptions which underlie the use of these instrumental variables (34-36):”
Background
RecUrrent Intra-articular Corticosteroid injections in Osteoarthritis: the RUbICOn mixed-methods study
Whitehouse, M. R., Judge, A., Hawley, S. J., Prats‐Uribe, A., Delmestri, A., Matharu, G. S., Moore, A. J., Palmer, C. K., Wylde, V., Anderson, E., et al. (2023) · Health Technology Assessment · doi:10.3310/lfaj9337 · Cited by 4
Stratification-based instrumental variable analysis framework for nonlinear effect analysis
Tian, H., Patel, A. R., & Burgess, S. (2024) · Biostatistics · doi:10.1093/biostatistics/kxaf043 · Cited by 3
“…must be satisfied: (i) the instrument is associated with the exposure; (ii) the instrument is not associated with the outcome via a confounding pathway; and (iii) the instrument affects the outcome only indirectly through the exposure, with no direct effect (Greenland, 2000; Martens et al., 2006).”
Lipoprotein(a) Is Associated With Increased Risk of Abdominal Aortic Aneurysm
Sharma, P., Judy, R., Yuan, S., Gellatly, C., Saxby, K. L., Rader, D. J., Bown, M. J., Levin, M. G., & Damrauer, S. M. (2026) · JACC Basic to Translational Science · doi:10.1016/j.jacbts.2025.101457 · Cited by 3
Effect of Treatment in a Specialized Pediatric Hemato-Oncology Setting on 5-Year Survival in Acute Lymphoblastic Leukemia: A Quasi-Experimental Study
van der Linde, M., van Leeuwen, N., Eijkenaar, F., Rijneveld, A. W., Pieters, R., & Karim‐Kos, H. E. (2022) · Cancers · doi:10.3390/cancers14102451 · Cited by 4
“We assessed the relevance and strength of our instrument using the F-statistic which, according to previous works, should exceed 10 for a sufficiently strong instrument [29,30].”
Method
Genetically Determined Rheumatoid Arthritis May Not Affect Heart Failure: Insights from Mendelian Randomization Study
Lin, X., Zhou, M., Zhang, C., & Li, J. (2023) · Global Heart · doi:10.5334/gh.1256 · Cited by 3
The causal role of gastroesophageal reflux disease in endometriosis: a bidirectional Mendelian randomization study
Shi, Z., Li, Z., Wang, K., & Yang, F. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1440157 · Cited by 4
“Instrumental variable (IV) selection satisﬁes correlation with exposure, and IVs should be independent of any confusion associated with the exposure result, which means that there are no causal pathways from IVs to results, except through exposure (24, 25).”
Is mobility transition driven by wealth inequality? Evidence from analysis of the electric two-wheelers adoption in India
Gupta, R., & Anand, A. (2025) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2025.104407 · Cited by 3
Association of Genetic Liability to Allergic Diseases with Overall and Early-Onset Colorectal Cancer Risk: A Mendelian Randomization Study
Alduhayh, S., Laskar, R. S., Jiang, X., Zhu, Z., Vincent, E. E., Constantinescu, A., Buchanan, D. D., Grant, R. C., Phipps, A. I., Brenner, H., et al. (2025) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-24-0970 · Cited by 3
“We performed several statistical tests to investigate the potential violation of the MR assumptions (22, 23).”
The effects of chairmen's social network heterogeneity on cooperative marketing innovation: evidence from Sichuan province in China
Liu, Y., & Li, H. (2020) · Journal of Agribusiness in Developing and Emerging Economies · doi:10.1108/jadee-06-2019-0085 · Cited by 3
The effect of bearing and rearing a child on blood pressure: a nationally representative instrumental variable analysis of 444 611 mothers in India
Teufel, F., Geldsetzer, P., Sudharsanan, N., Subramanyam, M. A., Yapa, H. M., Neve, J. D., Vollmer, S., & Bärnighausen, T. (2021) · International Journal of Epidemiology · doi:10.1093/ije/dyab058 · Cited by 3
“Instrumental variable (IV) analyses are a powerful approach for causal inference, which can be used when randomized controlled trials are not possible.(23,31,32) They exploit random variation in nature or due to policy or human practices for confounding control.”
BackgroundResultInfluential
Economic crisis and stillbirth ratios: Evidence from Southern Europe
Tsimbos, C., Verropoulou, G., & Petropoulou, D. (2021) · PLoS ONE · doi:10.1371/journal.pone.0259623 · Cited by 3
“While the OLS regression does not account for confounders affecting both the dependent and the independent variables (for instance, poverty levels) as would an instrumental variable approach, use of the latter technique is precluded by our small sample size, which can result in biased findings [39,40].”
Method
Valuing the impact of self-rated health and instrumental support on life satisfaction among the chinese population
Chan, C. H., & Wong, A. (2022) · BMC Public Health · doi:10.1186/s12889-022-13626-7 · Cited by 3
Bias and Confounding: Pharmacoepidemiological Study Using Administrative Database
Nojiri, S. (2015) · YAKUGAKU ZASSHI · doi:10.1248/yakushi.15-00006 · Cited by 3
Nutzung von Sekundärdaten für die pharmakoepidemiologische Forschung – machen wir das Beste draus!
Pigeot, I., Kollhorst, B., & Didelez, V. (2021) · Das Gesundheitswesen · doi:10.1055/a-1633-3827 · Cited by 3
“Angewendet in einer Studie zum Risiko gastrointestinaler Blutungen unter Therapie mit entweder traditionellen nicht-steroidalen Antirheumatika (tNSAID) oder selektiven COX-2-Hemmern konnte in GePaRD gezeigt werden, dass die Präferenz des Arztes für ein tNSAID im Vergleich zum COX-2-Hemmern alle Annahmen eines validen…”
BackgroundInfluential
Influences of family structure experiences on the risk of premarital sexual initiation during adolescence in Cameroon
Sidze, E., & Defo, B. K. (2013) · Advances in Life Course Research · doi:10.1016/j.alcr.2013.09.002 · Cited by 3
Sequence of novel agents in multiple myeloma: An instrumental variable analysis
Baz, R., Miladinović, B., Patel, A. M., Ho, V. Q., Shain, K. H., Alsina, M., Nishihori, T., Ochoa-Bayona, J. L., Sullivan, D. M., Dalton, W. S., et al. (2013) · Leukemia Research · doi:10.1016/j.leukres.2013.06.005 · Cited by 3
The Simple Geometry of Correlated Regressors and IV Corrections
Butler, R. (2016) · International Journal of Statistics in Medical Research · doi:10.6000/1929-6029.2016.05.03.4 · Cited by 3
“When we have more instrumental variables than correlated regressors, the over-identification test indicates whether those IVs provide coherent estimates in the sense of identifying the same coefficient [4-7].”
BackgroundMethod
Instrumental variable estimation of causal odds ratios using structural nested mean models
Matsouaka, R., & Tchetgen, E. J. T. (2017) · Biostatistics · doi:10.1093/biostatistics/kxw059 · Cited by 3
Score de propension : intérêts, utilisation et limites. Un guide pratique pour le clinicien
Moulis, G., & Lapeyre‐Mestre, M. (2018) · La Revue de Médecine Interne · doi:10.1016/j.revmed.2018.02.012 · Cited by 3
Improving Propensity Score Methods in Pharmacoepidemiology
Ali, M. S. (2014) · Utrecht University Repository (Utrecht University) · Cited by 3
Re: Comparative Effectiveness of Prostate Cancer Treatments: Evaluating Statistical Adjustments for Confounding in Observational Data
Weiss, N. S., & Hutter, C. M. (2011) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djr262 · Cited by 3
Economics and Epidemiology: Two Sides of the Same Coin or Different Currencies for Evaluating Impact?
Vigneri, M., Masset, E., Clarke, M. J., Exley, J., Tugwell, P., Welch, V. A., & White, H. (2018) · doi:10.51744/cip10 · Cited by 3
Exploring estrogen-related mechanisms in ovarian carcinogenesis: association between bone mineral density and ovarian cancer risk in a multivariable Mendelian randomization study
Tuesley, K., Webb, P. M., Protani, M. M., Donovan, P. J., Jordan, S. J., & Dixon‐Suen, S. C. (2024) · Cancer Causes & Control · doi:10.1007/s10552-024-01926-9 · Cited by 2
Causal Segment Regression with Multiple Thresholds
Wang, J. (2025) · Statistics and Computing · doi:10.1007/s11222-025-10673-z · Cited by 2
“The instrumental variable (IV) method is usually used to estimate the causal exposure effects in the presence of unmeasured confounding (Martens et al. 2006; Tan 2006; Ullman and Bentler 2012; Burgess et al. 2017), following the standard structural equation model (SEM) methodology (Duncan 1975;…”
Are digital payments driven by wealth inequality? Evidence from analysis of the unified payments interface (UPI) adoption in India
Gupta, R., Anand, A., & Gupta, T. (2025) · Economic Analysis and Policy · doi:10.1016/j.eap.2025.07.014 · Cited by 2
Postoperative pulmonary complications of desflurane- versus sevoflurane-based general anesthesia in patients with chronic obstructive pulmonary disease or asthma undergoing gastrointestinal cancer surgery: a nationwide retrospective cohort study
Makito, K., Matsuo, Y., Matsui, H., Fushimi, K., & Yasunaga, H. (2025) · Journal of Anesthesia · doi:10.1007/s00540-025-03548-0 · Cited by 2
The dual benefits of professional values on income and health among healthcare workers in China: a cross-sectional study
Lu, Y., Tang, X., Chen, M., Pei, S., Ju, K., Gu, T., Li, W., & Ma, L. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13658-w · Cited by 2
“This peer-based instrument has been used in similar contexts to identify causal effects in similar settings [48].”
Causal Mediation Analysis in Pregnancy Studies: the Case of Environmental Epigenetics
Valeri, L. (2017) · Current Epidemiology Reports · doi:10.1007/s40471-017-0112-1 · Cited by 2
Estimating treatment effects in the presence of unobserved confounders
Wang, J., Gao, W., Tang, M., & Liu, C. (2021) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610918.2021.1966465 · Cited by 2
“However, one of the main criteria for defining an instrumental variable is that it does not have a direct effect on the outcome variable, only indirectly through the treatment variable (see, Martens et al .”
Background
Opportunities and challenges in using instrumental variables to study causal effects in nonrandomized stress and trauma research.
Matthay, E. C., Smith, M. L., Glymour, M. M., White, J. S., & Gradus, J. L. (2022) · Psychological Trauma Theory Research Practice and Policy · doi:10.1037/tra0001370 · Cited by 2
Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation
Zhao, H., Zhang, Y., Cheng, D., Li, C., & Feng, Z. (2022) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc-dss-smartcity-dependsys57074.2022.00036 · Cited by 2
Advanced Considerations in Quantitative Methods for New Directions in Engineering Education Research
Katz, A., Godwin, A., & Brozina, C. (2023) · Routledge eBooks · doi:10.4324/9781003287483-36 · Cited by 3
Causal association between B cell count and psoriasis using two‐sample Mendelian randomization
Zhao, Z., Cheng, J. J., Zhu, J., Lü, S., Lv, H., & Wu, X. (2024) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.70089 · Cited by 2
Causal Links Between Bone Diseases and Temporomandibular Disorders
Zhang, L., Zhang, Q., & Zhao, D. (2025) · International Dental Journal · doi:10.1016/j.identj.2025.01.010 · Cited by 2
“18 A genetic variant can be considered as an instrumental variable for a given exposure if it satisﬁes the instrumental variable assumptions 19 (Figure 1): (1) it is not associated with the outcomes due to confounding pathways; (2) it does not affect the outcome unless through the exposure; (3) it…”
Effectiveness of drug testing requirements on pretrial supervision outcomes
Foudray, C. M. A., Lowder, E. M., & Wilson, D. B. (2025) · Journal of Criminal Justice · doi:10.1016/j.jcrimjus.2025.102386 · Cited by 2
Genetic evidence from Mendelian randomization links CD40 levels to increased risk of estrogen receptor-positive breast cancer
Yi, J., Chen, Q., Liu, X., Mao, Y., Wang, Y., Lv, M., Wang, H., & Wang, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-99410-0 · Cited by 2
“MR uses genetic variants as instrumental variables (IVs) to infer causality between an exposure and an outcome 13 , relying on certain assumptions: (1) the variant must be associated with the exposure; (2) it should not be associated with confounders that affect the outcome; and (3) it should…”
Unreliable evidence from problematic risk of bias assessments: Comment on Begh et al., ‘Electronic cigarettes and subsequent cigarette smoking in young people: A systematic review’
Egger, S. J., & McKee, M. (2025) · Addiction · doi:10.1111/add.70143 · Cited by 2
Unmeasured confounding and misclassification in studies estimating vaccine effectiveness against hospitalisation and death using electronic health records (EHRs): an evaluation of a multi-country European retrospective cohort study
Humphreys, J., Nicolay, N., Braeye, T., Evercooren, I. V., Hansen, C. H., Moustsen‐Helms, I. R., Sacco, C., Mateo‐Urdiales, A., Castilla, J., Martínez‐Baz, I., et al. (2025) · BMC Medical Research Methodology · doi:10.1186/s12874-025-02742-8 · Cited by 2
A Dose-Response Analysis of Crystalloid Administration during Esophageal Resection
Smith, M. T., & Nossaman, B. D. (2019) · Southern Medical Journal · doi:10.14423/smj.0000000000000991 · Cited by 2
The Validity and Efficiency of Hypothesis Testing in Observational Studies with Time-Varying Exposures
Campbell, H., & Gustafson, P. (2018) · Observational Studies · doi:10.1353/obs.2018.0010 · Cited by 2
“We also take the opportunity to discuss more nuanced considerations such as the “g-null paradox” of Robins (1986), and Martens et al. (2006)’s “limit on the strength of instruments.””
BackgroundInfluential
Pharmacoepidemiology
Garbe, E., Suissa, S., & Douros, A. (2022) · Springer eBooks · doi:10.1007/978-1-4614-6625-3_31-1 · Cited by 2
Modified inverse propensity weighting method to alleviate estimation errors in the model with multiple endogenous variables
Dhakal, B., McLeod, G. F. H., Insch, A., & Boden, J. M. (2023) · MethodsX · doi:10.1016/j.mex.2023.102513 · Cited by 1
Causal associations between sleep traits, sleep disorders, and glioblastoma: a two-sample bidirectional Mendelian randomization study
Chen, Y., Yu, W., Huang, Y., Jiang, Z., Deng, J., & Qi, Y. (2024) · Journal of Neurophysiology · doi:10.1152/jn.00338.2024 · Cited by 1
Higher Pregnancy Success Rates in Patients with Diminished Ovarian Reserve < 40 Years When Initially Treated by Intrauterine Insemination with Mild Ovarian Stimulation Compared to In Vitro Fertilization Alone: A Pilot Study
Deneer, J. J. M., le Cessie, S., van Santbrink, E. J. P., van der Westerlaken, L., & Lashley, L. (2025) · Reproductive Sciences · doi:10.1007/s43032-025-01830-w · Cited by 1
Uncovering the gut microbiota’s role in temporomandibular joint disorders: A bidirectional Mendelian randomization study
Ni, T., Shen, Z., Shi, H., & Han, Z. (2025) · Medicine · doi:10.1097/md.0000000000042590 · Cited by 1
Multiomics analyses of the complex interplay between genetic variants, DNA methylation, and gene expression in COVID-19
Chen, G., DeRoo, L. A., Goodney, G. A., Doumatey, A. P., Zhou, J., Adeyemo, A., Rotimi, C. N., & Gaye, A. (2025) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00206.2025 · Cited by 1
Study protocol for Controversies in psychiatry – antipsychotics: Long term outcomes and causal modelling of antipsychotic treatment using Norwegian Registry data
Nyttingnes, O., Hofstad, T., Johnsen, E., Strømme, M. F., Markussen, S., Killackey, E. J., Rinaldi, M., Blindheim, A. A., McDaid, D., Brinchmann, B., et al. (2025) · F1000Research · doi:10.12688/f1000research.166156.1 · Cited by 1
The Reporting and Methodological Recommendations for Observational Studies Estimating the Effects of Deprescribing Medications (REMROSE‐D) ISPE‐Endorsed Guidance
Hayes, K. N., Niznik, J. D., Gnjidic, D., Moriarty, F., Bennett, D., Laroche, M., Talbot, D. C., Alcusky, M. J., Sessa, M., Coe, A. B., et al. (2025) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.70255 · Cited by 1
“However, making sure a candidate instrument is truly an instrumental variable in a given study remains challenging [28].”
Impact of Prehospital Care Provided by Emergency Medical Services on Patient Survival in Trauma Cases
Althawadi, A. M. O., Maghrabi, O. F., Aljasem, A. S., Almulhem, N. A., Aljasim, M. Y. A., Surur, H. A. B., Alghamdi, Y. M. A., Alsharari Yousef Farhan, O., Alrashidi, M. M. S., & Atiyah, M. M. J. A. (2025) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.4296 · Cited by 1
Disentangled Instrumentalization Learning for Dialogue Emotion Detection
Nie, W., Peng, Z., Wei, Y., Wang, W., & Liu, A. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3669234 · Cited by 2
Checking Genetic Homogeneity Between Two Samples Using Summary Statistics With Application to Mendelian Randomization
Wang, K., & Wang, G. Z. (2026) · Statistics in Medicine · doi:10.1002/sim.70663 · Cited by 1
Correcting for Non-adherence in a Randomized Study of Hip Protectors to Prevent Fractures
Gill, K. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/q1d8-dp56 · Cited by 1
The Role of Historical Malaria in Institutions and Contemporary Economic Development
Gooch, E., Martínez-Vázquez, J., & Yedgenov, B. (2022) · Studies in Comparative International Development · doi:10.1007/s12116-022-09370-z · Cited by 1
Consumer and Professional Inflation Expectations – Properties and Mutual Dependencies
Rutkowska, A., Szyszko, M., & Próchniak, M. (2024) · Comparative Economic Research Central and Eastern Europe · doi:10.18778/1508-2008.27.23 · Cited by 1
“However, this choice is associated with inaccuracy due to large standard errors, bias when the sample size is small, and bias in large samples if one of the assumptions is only slightly violated (Martens et al. 2006).”
Background
The Determinants of Household Water Demand: A Focus on Water, Energy, and Food Prices
van Huyssteen, T., Thiam, D., & Nonhebel, S. (2024) · Water Economics and Policy · doi:10.1142/s2382624x24500140 · Cited by 1
The Causal Relationship Between Neurotrophic Factors and Delirium: A Mendelian Randomization Study
Wu, H., Jiang, R., Huang, X., & Hu, X. (2025) · Brain and Behavior · doi:10.1002/brb3.70494 · Cited by 1
A causal association between serum mitochondrial-related proteins and the risk of chronic kidney disease
Zhang, A., Zhang, Q., & Cao, M. (2025) · International Urology and Nephrology · doi:10.1007/s11255-025-04593-0 · Cited by 1
Causal inference with complex sampling designs
Brumback, B., & He, Z. (2012) · Cited by 1
Estimating Malaria Vaccine Efficacy in the Absence of a Gold Standard Case Definition: Mendelian Factorial Design
Hasegawa, R. B., & Small, D. S. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1863222 · Cited by 1
Reflecting on “A Statistician in Medicine” in 2020
Dempsey, W., & Mukherjee, B. (2020) · Statistics in Medicine · doi:10.1002/sim.8830 · Cited by 1
The Tenability of Counterhypotheses: A comment on Bross’ discussion of statistical criticism
Hill, J., & Hoggatt, K. J. (2018) · Observational Studies · doi:10.1353/obs.2018.0004 · Cited by 1
“Yet we know that when the assumptions of these methods fail to hold, things can go badly quickly (Angrist et al., 1996; Reardon and Raudenbush, 2013; Martens et al., 2006; Middleton et al., 2016).”
Research Methods: Epidemiologic Research in Geriatric Oncology
Bastiaannet, E. (2020) · Springer eBooks · doi:10.1007/978-3-319-57415-8_8 · Cited by 1
Rate of Intraoperative Crystalloid Administration During Thoracic Surgery Is Causal in Reducing Postoperative Hospital Length of Stay
Smith, M. T., Peairs, A., & Nossaman, B. D. (2023) · Ochsner Journal · doi:10.31486/toj.22.0113 · Cited by 1
Causal Effect Estimation Under Linear and Log-Linear Structural Nested Mean Models in the Presence of Unmeasured Confounding
Wang, C.-H. (2010) · ScholarlyCommons (University of Pennsylvania) · Cited by 1
Anxiety Disorders and Depression in Older Adults
Hek, K. (2013) · RePub (Erasmus University Rotterdam) · Cited by 1
Letter by Duyff and Groeneveld Regarding Article, “Proportion of Patients Treated With Thrombolysis in a Centralized Versus a Decentralized Acute Stroke Care Setting”
Duyff, R. F., & Groeneveld, C. (2012) · Stroke · doi:10.1161/strokeaha.112.660167 · Cited by 1
Do Active Communities Support Activity, or Support Active People?: Residential Self-selection in the Estimation of Built Environment Influences on Physical Activity
Boone‐Heinonen, J. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/9679-bh88 · Cited by 1
An exploration of the determinants and health impacts of active commuting
Martin, A. (2015) · UEA Digital Repository (University of East Anglia) · Cited by 1
Developing methods for causal mediation analysis of parenting interventions to improve child antisocial behaviour
Zhang, C. (2015) · Cited by 1
The effect of using fibrates in conjunction with statins for the management of dyslipidemia in persons with Type II diabetes mellitus
Suh, H. S. (2015) · University of Southern California Digital Library · doi:10.25549/usctheses-m2519 · Cited by 1
Using Latent Variable Models to Improve Causal Estimation
Oktay, H. (2021) · Scholarworks (University of Massachusetts Amherst) · doi:10.7275/11261040.0 · Cited by 1
Commentary: Using potential outcomes causal methods to assess whether reductions in PM2.5 result in decreased mortality
Goodman, J. E., Li, W., & Cox, L. A. (2021) · Global Epidemiology · doi:10.1016/j.gloepi.2021.100052 · Cited by 1
Board of Directors and cost of debt : the moderating effect of ownership structure - evidence from European SMEs
Malakeh, C. (2021) · Cited by 1
Bayesian Mendelian Randomization Analysis for Latent Exposures Leveraging GWAS Summary Statistics for Traits Co-Regulated by the ExposuresPreprint
Yu, Y., Lakkis, A., Zhao, B., & Jin, J. (2024) · medRxiv · doi:10.1101/2024.11.25.24317939 · Cited by 1
“…are required for an IV to be valid: it must be truly associated with the exposure, it only aﬀects the outcome through the exposure (no horizontal pleiotropy), and it is not associated with confounders of the association between the exposure and the outcome (“exchange-ability”) (6; 15; 33).”
Enhancing the use of routine program data for immunization decision-making: The role of coincidence analysis in localized learning and adaptation
Adamu, A. A., Ndwandwe, D. E., Uthman, O. A., & Wiysonge, C. S. (2025) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2025.2582302 · Cited by 1
Comparative Effectiveness of Alendronate and Risedronate on the Risk of Non-Vertebral Fractures in Older Women: An Instrumental Variables Approach: A Dissertation
Chen, Y. (2011) · eScholarship@UMassChan · doi:10.13028/j01k-pm54
Social Capital and Self-Rated Health. An IV Analysis
Redig, J., & Voon, M. (2014) · Lund University Publications Student Papers (Lund University)
Quasi-Experimental Health Policy Research: Evaluation of Universal Health Insurance and Methods for Comparative Effectiveness Research
Garabedian, L. F. (2013) · Digital Access to Scholarship at Harvard (DASH) (Harvard University)
Detection of and Adjustment for Multiple Unmeasured Confounding Variables in Logistic Regression by Bayesian Structural Equation Modeling
Kupek, E. J. (2015) · Journal of Advances in Medical and Pharmaceutical Sciences · doi:10.9734/jamps/2015/16185
HIV in Denmark and Greenland, 1995–2004, the effect of highly active antiretroviral therapy and characteristics of the HIV-infected population: an observational study
Lohse, N. (2006) · International Journal of Circumpolar Health · doi:10.3402/ijch.v65i5.18140
Fish et al. Respond
Fish, J., Ettner, S. L., Ang, A., & Brown, A. F. (2011) · American Journal of Public Health · doi:10.2105/ajph.2011.300090
Complications of hip and knee replacement surgery: a pharmaco-epidemiological approach
Lalmohamed, A. (2014) · Research Publications (Maastricht University)
Economic Shocks and Civil Conflict: Evidence from the Constraints of the Open-Economy Trilemma
Hull, P. (2010) · doi:10.14418/wes01.1.533
Pharmacoepidemiologic study of acute exacerbations of COPD: challenges and opportunities
Tsai, C. (2010) · Expert Review of Anti-infective Therapy · doi:10.1586/eri.10.112
For peer review only Estimating treatment effects in randomized controlled trials with non-compliance: a simulation study
BROWNE, G. B., & Thabane, L. (2014)
Association Between Adjuvant Chemotherapy and Nephrotoxicity and Kidney Function Monitoring in Elderly Breast Cancer Patients
Li, S. (2013) · University of Minnesota Digital Conservancy (University of Minnesota)
Mathematical reflection approach to instrumental variable estimation method for simple regression model
Saqr, A., & Khan, S. (2016) · University of Southern Queensland ePrints (University of Southern Queensland)

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Instrumental Variables: Application and Limitations

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks