Verification problems in diagnostic accuracy studies: consequences and solutionsBMJ 2011; 343 doi: https://doi.org/10.1136/bmj.d4770 (Published 02 August 2011) Cite this as: BMJ 2011;343:d4770
- Joris A H de Groot, clinical epidemiologist1,
- Patrick M M Bossuyt, professor of clinical epidemiology2,
- Johannes B Reitsma, associate professor of clinical epidemiology2,
- Anne W S Rutjes, senior researcher3,
- Nandini Dendukuri, assistant professor clinical epidemiology and biostatistics4,
- Kristel J M Janssen, clinical epidemiologist1,
- Karel G M Moons, professor of clinical epidemiology1
- 1Julius Center for Health Sciences and Primary care, UMC Utrecht, PO Box 85500, 3508GA Utrecht, Netherlands
- 2Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center Amsterdam, 1100 DE Amsterdam, Netherlands
- 3Division of Clinical Epidemiology and Biostatistics, Institute of Social and Preventive Medicine-University of Bern, 3012 Bern, Switzerland
- 4Royal Victoria Hospital, Quebec, Canada H3A 1A1
- Correspondence to: J A H de Groot
- Accepted 2 May 2011
The accuracy of a diagnostic test or combination of tests (such as in a diagnostic model) is the ability to correctly identify patients with or without the target disease. In studies of diagnostic accuracy, the results of the test or model under study are verified by comparing them with results of a reference standard, applied to the same patients, to verify disease status (see first panel in figure⇓).1 Measures such as predictive values, post-test probabilities, ROC (receiver operating characteristics) curves, sensitivity, specificity, likelihood ratios, and odds ratios express how well the results of an index test agree with the outcome of the reference standard.2 Biased and exaggerated estimates of diagnostic accuracy can lead to inefficiencies in diagnostic testing in practice, unnecessary costs, and physicians making incorrect treatment decisions.
The reference standard ideally provides error-free classification of the disease outcome presence or absence. In some cases, it is not possible to verify the definitive presence or absence of disease in all patients with the (single) reference standard, which may result in bias. In this paper, we describe the most important types of disease verification problems using examples from published diagnostic accuracy studies. We also propose solutions to alleviate the associated biases.
Often not all study subjects who undergo the index test receive the reference standard, leading to missing data on disease outcome (see middle panel in figure⇑). The bias associated with such situations of partial verification is known as partial verification bias, work-up bias, or referral bias.3 4 5
Clinical examples of partial verification
Various mechanisms can lead to partial verification (see examples in table 1⇓).
When the condition of interest …