Rationale
Assessing the performance of diagnostic tests requires evaluation of the
amount of diagnostic uncertainty the test reduces (i.e. 0% - useless
test, 100% - perfect test). Statistical measures currently dominating
the evidence-based medicine (EBM) field and particularly meta-analysis
(e.g. sensitivity and specificity), cannot explicitly measure this
uncertainty reduction. Mutual information (MI), an information theory
statistic, is a more appropriate metric for evaluating diagnostic tests
as it explicitly quantifies uncertainty and, therefore, facilitates
natural interpretation of a test’s value. In this paper, we propose the
use of MI as a single measure to express diagnostic test performance and
demonstrate how it can be used in meta-analysis of diagnostic test
studies.