alexa Instrumental Variable Analysis in Epidemiologic Studies: An Overview of the Estimation Methods
ISSN : 2153-2435

Pharmaceutica Analytica Acta
Open Access

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Review Article

Instrumental Variable Analysis in Epidemiologic Studies: An Overview of the Estimation Methods

Klungel OH1,2*, Jamal Uddin M1, de Boer A1, Belitser SV1, Groenwold RH1,2 and Roes KC2

1Devision of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands

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

*Corresponding Author:
Klungel OH
Division of Pharmacoepidemiology and Clinical Pharmacology
University of Utrecht, Utrecht, Netherlands
Tel: +31685384692
Fax: +31-30 253 9166
E-mail: [email protected]

Received date: January 06, 2015; Accepted date: March 08, 2015; Published date: March 15, 2015

Citation: Klungel OH, Jamal Uddin M, de Boer A, Belitser SV, Groenwold RH et al. (2015) Instrumental Variable Analysis in Epidemiologic Studies: An Overview of the Estimation Methods. Pharm Anal Acta 6:353. doi: 10.4172/2153-2435.1000353

Copyright: © 2015 Klungel OH, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Instrumental variables (IV)analysis seems an attractive method to control for unmeasured confounding in observational epidemiological studies. Here, we provide an overview of the estimation methods of IVanalysis and indicate their possible advantages and limitations.We found that two-stage least squares is the method of first choice if exposure and outcome are both continuous and show a linear relation. In case of a nonlinear relation, two-stage residual inclusion may be a suitable alternative. In settings with binary outcomes as well as nonlinear relations between exposure and outcome, generalized method of moments (GMM), structural mean models (SMM), and bivariate probit models perform well, yet GMM and SMM are generally more robust. The standard errors of the IVestimate can be estimated using a robust or bootstrap method. All estimation methods are prone to bias when the IVassumptions are violated. Researchers should be aware of the underlying assumptions of the estimation methods as well as the key assumptions of the IVwhen interpreting the exposure effects estimated through IV analysis.

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