Epidemiologic studies are increasingly used to investigate the safety and effectiveness of medical products and interventions. Appropriate adjustment for confounding in such studies is challenging because exposure is determined by a complex interaction of patient, physician, and healthcare system factors. The challenges of confounding control are particularly acute in studies using healthcare utilization databases where information on many potential confounding factors is lacking and the meaning of variables is often unclear. We discuss advantages and disadvantages of different approaches to confounder control in healthcare databases. In settings where considerable uncertainty surrounds the data or the causal mechanisms underlying the treatment assignment and outcome process, we suggest that researchers report a panel of results under various specifications of statistical models. Such reporting allows the reader to assess the sensitivity of the results to model assumptions that are often not supported by strong subject-matter knowledge.
From the *Department of Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital & Harvard Medical School, Boston, MA; and †Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, NC.
Supported by the National Institute of Aging (K25-AG027400 to M.A.B., RO1-AG023178 to T.S., and R01-AG18833 to R.J.G.) and the National Library of Medicine (RO1-LM010213 to SS). Dr. Brookhart and Stürmer are also supported by the UNC-GSK Center of Excellence in Pharmacoepidemiology and Public Health.
Reprints: M. Alan Brookhart, PhD, Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, McGavran-Greenberg, CB 7435 Chapel Hill, NC 27599-7435. E-mail: firstname.lastname@example.org.