Healthy User and Related Biases in Observational Studies of Preventive Interventions: A Primer for Physicians

• Published in: Journal of general internal medicine : JGIM Vol. 26; no. 5; pp. 546 - 550
• Main Authors: Shrank, William H., Patrick, Amanda R., Alan Brookhart, M.
• Format: Journal Article
• Language: English
• Published: New York Springer-Verlag 01.05.2011; Springer; Springer Nature B.V
• Subjects: Bias; Biological and medical sciences; Design of experiments; Effectiveness studies; General aspects; Health Behavior; Health participants; Humans; Internal Medicine; Medical sciences; Medicine; Medicine & Public Health; Patient Participation; Perspectives; Physicians - standards; Prevention and actions; Preventive medicine; Preventive Medicine - standards; Public health. Hygiene; Public health. Hygiene-occupational medicine; Research Design - standards; Research methodology; Influenza Vaccination; Comparative Effectiveness Research; Statin; Preventive Therapy; Hormone Replacement Therapy; Human; Medicine; Prevention; Observational study; User; Healthy subject; Health staff; Bias; Use; Primer; Physician
• ISSN: 0884-8734; 1525-1497; 1525-1497
• DOI: 10.1007/s11606-010-1609-1

The current emphasis on comparative effectiveness research will provide practicing physicians with increasing volumes of observational evidence about preventive care. However, numerous highly publicized observational studies of the effect of prevention on health outcomes have reported exaggerated relationships that were later contradicted by randomized controlled trials. A growing body of research has identified sources of bias in observational studies that are related to patient behaviors or underlying patient characteristics, known as the healthy user effect, the healthy adherer effect, confounding by functional status or cognitive impairment, and confounding by selective prescribing. In this manuscript we briefly review observational studies of prevention that have appeared to reach incorrect conclusions. We then describe potential sources of bias in these studies and discuss study designs, analytical methods, and sensitivity analyses that may mitigate bias or increase confidence in the results reported. More careful consideration of these sources of bias and study designs by providers can enhance evidence-based decision-making.