Estimation of diagnostic accuracy of a combination of continuous biomarkers allowing for conditional dependence between the biomarkers and the imperfect reference‐test

Summary Estimating biomarker‐index accuracy when only imperfect reference‐test information is available is usually performed under the assumption of conditional independence between the biomarker and imperfect reference‐test values. We propose to define a latent normally‐distributed tolerance‐variable underlying the observed dichotomous imperfect reference‐test results. Subsequently, we construct a Bayesian latent‐class model based on the joint multivariate normal distribution of the latent tolerance and biomarker values, conditional on latent true disease status, which allows accounting for conditional dependence. The accuracy of the continuous biomarker‐index is quantified by the AUC of the optimal linear biomarker‐combination. Model performance is evaluated by using a simulation study and two sets of data of Alzheimer's disease patients (one from the memory‐clinic‐based Amsterdam Dementia Cohort and one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database). Simulation results indicate adequate model performance and bias in estimates of the diagnostic‐accuracy measures when the assumption of conditional independence is used when, in fact, it is incorrect. In the considered case studies, conditional dependence between some of the biomarkers and the imperfect reference‐test is detected. However, making the conditional independence assumption does not lead to any marked differences in the estimates of diagnostic accuracy.