Sensitivity analysis and external adjustment for unmeasured confounders in epidemiologic database studies of therapeutics

TLDR

Large health‑care utilization databases are commonly used to study drug effects, but the lack of detailed clinical, lifestyle, or OTC medication data leads to residual confounding bias. This paper presents a systematic framework for sensitivity analyses to assess the impact of residual confounding in pharmacoepidemiologic studies using such databases. The authors outline four strategies: assumption‑based arrays, quantifying the confounding strength required to explain observed associations, algebraic external adjustment for single binary confounders, and propensity‑score calibration for joint distributions of multiple confounders. Their findings indicate that applying these sensitivity analyses and external adjustments improves understanding of drug effects and should replace qualitative discussions of residual confounding. © 2006 John Wiley & Sons, Ltd.

Abstract

Abstract Background Large health care utilization databases are frequently used to analyze unintended effects of prescription drugs and biologics. Confounders that require detailed information on clinical parameters, lifestyle, or over‐the‐counter medications are often not measured in such datasets, causing residual confounding bias. Objective This paper provides a systematic approach to sensitivity analyses to investigate the impact of residual confounding in pharmacoepidemiologic studies that use health care utilization databases. Methods Four basic approaches to sensitivity analysis were identified: (1) sensitivity analyses based on an array of informed assumptions; (2) analyses to identify the strength of residual confounding that would be necessary to explain an observed drug‐outcome association; (3) external adjustment of a drug‐outcome association given additional information on single binary confounders from survey data using algebraic solutions; (4) external adjustment considering the joint distribution of multiple confounders of any distribution from external sources of information using propensity score calibration. Conclusion Sensitivity analyses and external adjustments can improve our understanding of the effects of drugs and biologics in epidemiologic database studies. With the availability of easy‐to‐apply techniques, sensitivity analyses should be used more frequently, substituting qualitative discussions of residual confounding. Copyright © 2006 John Wiley & Sons, Ltd.