Future challenges for diagnostic research: striking a balance between simplicity and complexity
- Population and Public Health Branch, Health Canada, AL#1918C3, Tunney's Pasture, Ottawa, Ontario K1A 0K9, Canada; Department of Public Health Sciences, University of Toronto; Department of Epidemiology and Community Medicine, University of Ottawa, Canada
- Correspondence to: Dr B C K Choi (Bernard_Choi{at}hc-sc.gc.ca)
The bridge from complex models to the clinicians' practice
In this issue of the journal, Feinstein has provided great insight and correctly pointed out a number of problem areas in current research on diagnostic tests1: dichotomising a disease state into yes and no, and a test result into positive and negative, do not represent the real clinical situation. Sensitivity, specificity, and likelihood ratios are calculated from patients with known disease status and therefore do not help practising physicians to make a diagnosis. Many research studies on diagnostic tests assume a disease prevalence (prior probability) of 0.5, which is unrealistic. The premise of diagnostic research, that sensitivity and specificity of a diagnostic test is constant, is now known to be wrong. Practising physicians often want to avoid the correct and academically recommended methods because of complex computations. Many diagnostic tests can produce additional information, but it is usually disregarded in the assessment of the tests. Tests in combination should be evaluated differently than a single test. Tests used to “rule out” or to “rule in” a disease should be evaluated differently. Tests used to identify the stage of a disease, or to offer reassurance rather than diagnosis, cannot be evaluated with conventional indices of accuracy. Tests based on subjective decisions are difficult to assess. Many research studies fail to comply with appropriate methodological standards and are often …
