Verification bias

In statistics, verification bias is a type of measurement bias in which the results of a diagnostic test affect whether the gold standard procedure is used to verify the test result. This type of bias is also known as "work-up bias" or "referral bias".^[1]

In clinical practice, referral bias is more likely to occur when a preliminary diagnostic test is negative. Because many gold standard tests can be invasive, expensive, and carry a higher risk (e.g. angiography, biopsy, surgery), patients and physicians may be more reluctant to undergo further work-up if a preliminary test is negative.

In cohort studies, obtaining a gold standard test on every patient may not always be ethical, practical, or cost effective. These studies can thus be subjected to verification bias. One method to limit verification bias in clinical studies is to perform gold standard testing in a random sample of study participants.

In most situations, verification bias introduces a sensitivity estimate that is too high and a specificity that is too low.^[2]

References

↑ Begg CB, Greenes RA (1983). "Assessment of diagnostic tests when disease verification is subject to selection bias". Biometrics. 39 (1): 207–215. doi:10.2307/2530820. PMID 6871349.
↑ Zhou XH (1998). "Correcting for verification bias in studies of a diagnostic test's accuracy". Statistical Methods in Medical Research. 7 (4): 337‐353. doi:10.1191/096228098676485370.

Biases

Lists of biases	General Memory

Cognitive biases	Acquiescence Anchoring Attentional Attribution Authority Automation Belief Blind spot Choice-supportive Confirmation Congruence Cultural Distinction Egocentric Emotional Extrinsic incentives Fading affect Correspondence Halo effect Hindsight Hostile attribution Impact In-group Negativity Normalcy Optimism Outcome Precision Pro-innovation Response Restraint Self-serving Social comparison Status quo Time-saving Trait ascription Zero-risk In animals

Statistical biases	Estimator Forecast Healthy user Information Lead time Length time Non-response Omitted-variable Participation Recall Sampling Selection Self-selection Social desirability Spectrum Survivorship Systematic error Systemic Verification Wet

Other biases	Academic Exponent Funding FUTON Inductive Infrastructure Inherent in education Media Net Publication Reporting White hat

Bias reduction	Cognitive bias mitigation Debiasing Heuristics in judgment and decision-making

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