Aarhus University Seal / Aarhus Universitets segl

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

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

DOI

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.

Original languageEnglish
JournalClinical epidemiology
Volume9
Pages (from-to)185-193
Number of pages9
ISSN1179-1349
DOIs
Publication statusPublished - 2017

    Research areas

  • Journal Article

See relations at Aarhus University Citationformats

ID: 114486556