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Clin Epidemiol. 2017 Mar 28;9:185-193. doi: 10.2147/CLEP.S129879. eCollection 2017.

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

Author information

1
Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark.
2
Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark; Department of Clinical Epidemiology, Leiden University Medical Center, The Netherlands; Department 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:

confounding; health care databases; observational studies

Conflict of interest statement

Disclosure The authors report no conflicts of interest in this work.

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