Abstract:
The traditional concept of describing the validity of a diagnostic test neglects the
presence of chance agreement between test result and true (disease) status. Sensitivity
and specificity, as the fundamental measures of validity, can thus only be considered
in conjunction with each other to provide an appropriate basis for the evaluation
of the capacity of the test to discriminate truly diseased from truly undiseased subjects.
In this paper, chance-corrected analogues of sensitivity and specificity are presented
as supplemental measures of validity, which pay attention to the problem of chance
agreement and offer the opportunity to be interpreted separately. While recent proposals
of chance-correction techniques, suggested by several authors in this context, lead
to measures which are dependent on disease prevalence, our method does not share this
major disadvantage. We discuss the extension of the conventional ROC-curve approach
to chance-corrected measures of sensitivity and specificity. Furthermore, point and
asymptotic interval estimates of the parameters of interest are derived under different
sampling frameworks for validation studies. The small sample behavior of the estimates
is investigated in a simulation study, leading to a logarithmic modification of the
interval estimate in order to hold the nominal confidence level for small samples.
Keywords
Diagnostic Test - ROC curve - Sensitivity - Specificity - Validity
