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Efficient sampling approaches to address confounding in database studies

Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada, james.hanley{at}mcgill.ca

Nandini Dendukuri

Technology Assessment Unit, McGill University Health Centre, and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada

Administrative and other population-based databases are widely used in pharmacoepidemiology to study the unintended effects of medications. They allow investigators to study large case series, and they document prescription medication exposure without having to contact individuals or medical charts, or rely on human recall. However, such databases often lack information on potentially important confounding variables. This review describes some of the sampling approaches and accompanying data-analysis methods that can be used to assess, and deal efficiently with, such confounding.

This version was published on February 1, 2009

Statistical Methods in Medical Research, Vol. 18, No. 1, 81-105 (2009)
DOI: 10.1177/0962280208096046


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