Abstract
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. Methods We use two cases from
the literature to demonstrate the applicability of MI meta-analysis in
assessing diagnostic performance. These cases are: 1) Meta-analysis of
studies evaluating ultrasonography (US) to detect endometrial cancer and
2) meta-analysis of studies evaluating magnetic resonance angiography to
detect arterial stenosis. Results Results produced by the MI
meta-analyses are comparable to the results of meta-analyses based on
traditionally used statistical measures. However, the results of MI are
easier to understand as it relates directly to the extent of uncertainty
a diagnostic test can reduce. For example, a US test diagnosing
endometrial cancer is 40% specific and 94% sensitive. The combination
of these values is difficult to interpret and may lead to inappropriate
assessment (e.g. one could favour the test due to its high sensitivity,
ignoring its low specificity). In terms of MI however, the test reduces
diagnostic uncertainty by 10%, which is marginal and thus the test is
clearly not very useful. Conclusions We have demonstrated the
suitability of MI in assessing the performance of diagnostic tests,
which can facilitate easier interpretation of the true utility of
diagnostic tests.