Confounding Control in Healthcare Database Research

TLDR

Epidemiologic studies increasingly assess medical product safety and effectiveness, yet confounding adjustment is difficult because exposure depends on complex patient, physician, and system factors, especially in healthcare utilization databases where many confounders are missing and variable meanings are unclear. The authors aim to evaluate the pros and cons of various confounder‑control methods in healthcare databases and recommend reporting multiple model specifications to assess sensitivity. They review and compare different confounder‑control strategies, highlighting their strengths and limitations, and propose presenting results from several model specifications. Reporting multiple specifications allows readers to gauge how sensitive findings are to model assumptions that lack strong subject‑matter support.

Abstract

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.