A methodological framework to distinguish spectrum effects from spectrum biases and to assess diagnostic and screening test accuracy for patient populations: application to the Papanicolaou cervical cancer smear test.
BACKGROUND
A spectrum effect was defined as differences in the sensitivity or specificity of a diagnostic test according to the patient's characteristics or disease features. A spectrum effect can lead to a spectrum bias when subgroup variations in sensitivity or specificity also affect the likelihood ratios and thus post-test probabilities. We propose and illustrate a methodological framework to distinguish spectrum effects from spectrum biases.


METHODS
Data were collected for 1781 women having had a cervical smear test and colposcopy followed by biopsy if abnormalities were detected (the reference standard). Logistic models were constructed to evaluate both the sensitivity and specificity, and the likelihood ratios, of the test and to identify factors independently affecting the test's characteristics.


RESULTS
For both tests, human papillomavirus test, study setting and age affected sensitivity or specificity of the smear test (spectrum effect), but only human papillomavirus test and study setting modified the likelihood ratios (spectrum bias) for clinical reading, whereas only human papillomavirus test and age modified the likelihood ratios (spectrum bias) for "optimized" interpretation.


CONCLUSIONS
Fitting sensitivity, specificity and likelihood ratios simultaneously allows the identification of covariates that independently affect diagnostic or screening test results and distinguishes spectrum effect from spectrum bias. We recommend this approach for the development of new tests, and for reporting test accuracy for different patient populations.
Elie
Caroline
C
Department of Biostatistics, Groupe hospitalier Cochin - Saint Vincent de Paul and Université Paris-Descartes, Paris, France. caroline.elie@univ-paris5.fr
Coste
Joël
J
D016001:Confidence Intervals; D005260:Female; D006801:Humans; D016013:Likelihood Functions; D016015:Logistic Models; D065006:Papanicolaou Test; D012680:Sensitivity and Specificity; D002583:Uterine Cervical Neoplasms / Q000175:diagnosis*; D014626:Vaginal Smears / Q000592:standards*