For citation purposes: Groenwold RHH, Uddin MJ, Roes KCB, de Boer A, Rivero-Ferrer E, Martin E, Gatto NM, Klungel OH. Instrumental variable analysis in randomized trials with non-compliance and observational pharmacoepidemiologic studies. OA Epidemiology 2014 May 09;2(1):9.


Statistical/Methodological Debate

Instrumental variable analysis in randomized trials with non-compliance and observational pharmacoepidemiologic studies.

R Groenwold,, M Uddin, K Roes, A de Boer, E Rivero-Ferrer, E Martin, N Gatto, O Klungel,

Authors affiliations

(1) Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands

(2) Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands

(3) Global Clinical Epidemiology, Novartis Farmaceutica S.A., Barcelona, Spain

(4) BIFAP (Base de Datos para la Investigación Farmacoepidemiológica en Atención Primaria), Pharmacoepidemiology and Pharmacovigilance Division, Medicines for Human Use Department Agencia Española de Medicamentos y Productos Sanitarios (AEMPS), Madrid, Spain

(5) Epidemiology, Worldwide Safety & Regulatory, Pfizer Inc, New York, NY, USA

* Corresponding author Email:



Instrumental variable (IV) analysis potentially accounts for unmeasured confounding in observational studies, but it can also control for non-compliance in randomized trials.

IV analysis requires that the IV is related to treatment status, yet independent of confounders of the treatment-outcome relation. This implies that in pharmacoepidemiologic scenarios where IV analysis is needed the most (because of strong unmeasured confounding), IVs will typically be weakly associated with treatment. Furthermore, IV analysis assumes that the IV affects the outcome only through the treatment under study. A common IV in pharmacoepidemiological studies is the physician prescribing preference, which for the latter assumption implies that physicians only differ in their preference for the treatment under study, but they do not differ with respect to e.g. preferences for concomitant treatments, skills, organization of their practice, etc. Assumptions underlying IV assumptions need thorough evaluation before proceeding with IV analyses. Here, IV analysis is illustrated, its key assumptions are illustrated by a randomized trial with non-compliance, and the utility of IVs for observational pharmacoepidemiologic studies is discussed.


The validity and applicability of IV analysis in observational pharmacoepidemiologic studies still have to be established, which requires more applications of IV analysis and debate on the likelihood of the assumptions underlying IV analysis.

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