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Confounding in observational studies based on large health care databases: problems and potential solutions – a primer for the clinician

Authors Nørgaard M, Ehrenstein V, Vandenbroucke JP

Received 10 December 2016

Accepted for publication 15 February 2017

Published 28 March 2017 Volume 2017:9 Pages 185—193

DOI https://doi.org/10.2147/CLEP.S129879

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Colin Mak

Peer reviewer comments 3

Editor who approved publication: Professor Irene Petersen


Mette Nørgaard,1 Vera Ehrenstein,1 Jan P Vandenbroucke1–3

1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; 2Department of Clinical Epidemiology, Leiden University Medical Center, The Netherlands; 3Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, United Kingdom

Abstract: Population-based health care databases are a valuable tool for observational studies as they reflect daily medical practice for large and representative populations. A constant challenge in observational designs is, however, to rule out confounding, and the value of these databases for a given study question accordingly depends on completeness and validity of the information on confounding factors. In this article, we describe the types of potential confounding factors typically lacking in large health care databases and suggest strategies for confounding control when data on important confounders are unavailable. Using Danish health care databases as examples, we present the use of proxy measures for important confounders and the use of external adjustment. We also briefly discuss the potential value of active comparators, high-dimensional propensity scores, self-controlled designs, pseudorandomization, and the use of positive or negative controls.

Keywords: observational studies, health care databases, confounding

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