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

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. KEYWORDScouple-based study, environmental pollutants, kernel machine regression, nonparametric regression, REML 1374

Reconsidering evidence-based management of endometriosis

“…This is illustrated by a recent Cochrane review, suggesting using tests sequentially, beginning with the test with the highest sensitivity, and then re-testing the negative group ( Nisenblat et al 2016 ). Although the added value of a second test and the combined accuracy of tests can be calculated with a Bayesian approach ( Broemeling, 2011 ) as illustrated recently for endometriosis ( Chen and Hwang, 2019 ; Chen et al, 2019 ), this is still rarely used.…”

Hierarchy of evidence for endometriosis diagnosis and surgery.

“…The difficulty was illustrated in a recent Cochrane review, suggesting to use tests sequentially, using first a test with high sensitivity, and subsequently using another test to re-test the negative group (24). The added value and the combined accuracy of tests can be calculated with a Bayesian approach as demonstrated for endometriosis (25,26), but this is overall rarely used.…”

Bayesian network modelling for early diagnosis and prediction of Endometriosis

“…Literature was reviewed which claimed to be using Bayesian approaches but were not centred on the concept of casual Bayesian Network models. Correlated modelling framework to estimate ROC curves and the associated area under the curves was applied to the diagnostic performance of physicians in their diagnosing of endometriosis which suggested that clinical information may play a more important role in physicians' diagnostic performance than their experiences (Chen, et al, 2019). Endometriosis phenotypes were learned from patient-generated data through unsupervised learning (Urteaga, et al, 2020).…”

“…Endometriosis is a relatively common and potentially debilitating gynaecological disorder that affects 6-10% of women of reproductive age (Rogers, et al, 2013). It is present in about a quarter of women who are infertile (Chen, et al, 2019). Endometriosis is a long-term condition defined as the presence of endometrial-like tissue outside the uterus and it is associated with a chronic and inflammatory reaction.…”