Healthy user bias

The healthy user bias or healthy worker bias is a bias that can damage the validity of epidemiologic studies testing the efficacy of particular therapies or interventions.

Specifically, it is a sampling bias or selection bias: the kind of subjects that voluntarily enroll in a clinical trial and actually follow the experimental regimen are not representative of the general population. People who volunteer for a study can be expected, on average, to be healthier than people who don't volunteer, as they are concerned for their health and are predisposed to follow medical advice,^[1] both factors that would aid one's health. In a sense, being healthy or active about one's health is a precondition for becoming a subject of the study, an effect that can appear under other conditions such as studying particular groups of workers. For example, someone in ill health is unlikely to have a job as manual laborer. As a result, studies of manual laborers are studies of people who are currently healthy enough to engage in manual labor, rather than studies of people who would do manual labor if they were healthy enough.

References[]

^ Shrank, William H.; Patrick, Amanda R.; Alan Brookhart, M. (May 2011). "Healthy User and Related Biases in Observational Studies of Preventive Interventions: A Primer for Physicians". Journal of General Internal Medicine. 26 (5): 546–550. doi:10.1007/s11606-010-1609-1. ISSN 0884-8734. PMC 3077477. PMID 21203857.

External links[]

"Do We Really Know What Makes Us Healthy?"

[1] Shrank, William H.; Patrick, Amanda R.; Alan Brookhart, M. (May 2011). "Healthy User and Related Biases in Observational Studies of Preventive Interventions: A Primer for Physicians". Journal of General Internal Medicine. 26 (5): 546–550. doi:10.1007/s11606-010-1609-1. ISSN 0884-8734. PMC 3077477. PMID 21203857.

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Cognitive biases	Actor–observer Acquiescence Ambiguity Anchoring Attentional Attribution Authority Automation Belief Blind spot Choice-supportive Confirmation Compassion fade Congruence Courtesy Cultural Distinction Dunning–Kruger Egocentric Emotional Extrinsic incentives Fading affect Framing Frequency Correspondence Halo effect Hindsight Horn effect Hostile attribution Impact Implicit In-group Mere-exposure effect Negativity Normalcy Omission Optimism Out-group homogeneity Outcome Overton window Precision Present Pro-innovation Response Restraint Self-serving Social comparison Status quo Time-saving Trait ascription von Restorff effect Zero-risk In animals
Statistical biases	Estimator Forecast Healthy user Information Psychological Lead time Length time Non-response Observer Omitted-variable Participation Recall Sampling Selection Self-selection Social desirability Spectrum Survivorship Systematic error Systemic Verification Wet
Other biases	Academic Funding FUTON Inductive Infrastructure Inherent In education Media False balance Vietnam War Norway South Asia Sweden United States Arab–Israeli conflict Ukraine Net Political bias Publication Reporting White hat
Bias reduction	Cognitive bias mitigation Debiasing Heuristics in judgment and decision-making
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Healthy user bias

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