A correlated Bayesian rank likelihood approach to multiple ROC curves for endometriosis

Zhen Chen, Beom Seuk Hwang, Hwang BS, Kim S, Sungduk Kim
Statistics in medicine · 2018 · vol. 38(8) , pp. 1374–1385 · doi:10.1002/sim.8043 · PMID:30421556 · W2901810579
Diagnostic accuracy OA: closed CC0 ⤵ 8 in-corpus citations
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This paper proposes a correlated Bayesian rank likelihood approach to model multiple ROC curves, finding that physicians' diagnostic performance for endometriosis varied by session but not by group, suggesting clinical information is more influential than experience.

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Abstract

In analysis of diagnostic data with multiple tests, it is often the case that these tests are correlated. Modeling the correlation explicitly not only produces valid inference results but also enables borrowing of information. Motivated by the Physician Reliability Study (PRS) that investigated the diagnostic performance of physicians in diagnosing endometriosis, we construct a correlated modeling framework to estimate ROC curves and the associated area under the curves. This correlated approach is quite appealing for the PRS data set that suffers from the problem of small sample sizes, as it enables information borrowing between physician groups and sessions. Given that the test scores appear to be non-normal even after logarithm transformation, we use the ranks of the data to conduct likelihood estimation and inference. We use the deviance information criterion to select competing models and conduct simulation studies to assess model performances. In application to the PRS data set, we found that the physicians are not significantly different in their diagnostic performance between groups; however, they are different between the sessions. This suggests that clinical information may play a more important role in physicians' diagnostic performance than their experiences. Our empirical evidence also demonstrates that when using both woman- and physician-specific random effects, the model parameter estimates are much smoother.

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Condition tags

endometriosis

MeSH descriptors

Bayes Theorem Bayes Theorem Endometriosis Endometriosis ROC Curve ROC Curve Female Female Humans Humans Likelihood Functions Likelihood Functions Models, Statistical Models, Statistical Reproducibility of Results Reproducibility of Results

Citation neighborhood (2-hop)

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

References (13)

Cited by (8)

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