Confounding control in healthcare database research: challenges and potential approaches.
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.
Brookhart
M Alan
MA
Department of Medicine, Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital & Harvard Medical School, Boston, MA, USA. abrookhart@unc.edu
Stürmer
Til
T
Glynn
Robert J
RJ
Rassen
Jeremy
J
Schneeweiss
Sebastian
S
D015984:Causality; D003071:Cognition; D015986:Confounding Factors, Epidemiologic; D016208:Databases, Factual / Q000706:statistics & numerical data*; D006296:Health Services / Q000706:statistics & numerical data*; D006801:Humans; D055118:Medication Adherence; D018579:Patient Selection; D055553:Prescription Drugs / Q000627:therapeutic use*; D012720:Severity of Illness Index; D016896:Treatment Outcome
D055553:Prescription Drugs
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