Statistical evaluation of diagnostic tests under verification bias

The use of diagnostic tests to discriminate between disease classes is becoming more and more popular in medicine, which leads to the urgent need for assessing accuracy of diagnostic tests before their implementation. To do that, a common tool is receiver operating characteristic (ROC) analysis. More precisely, the ROC curve and the area under the ROC curve (AUC) are commonly employed when two disease classes (typically, non-diseased and diseased) are considered, whereas the ROC surface and the volume under the ROC surface (VUS) are frequently used when the disease status has three categories (e.g., non-diseased, intermediate and diseased). In estimating such parameters, we assume that the true disease status of each patient can be determined by means of a gold standard test. In practice, unfortunately, the true disease status could be unavailable for all study subjects, due to the expensiveness or invasiveness of the gold standard test. Thus, often only a subset of patients undergoes disease verification. Statistical evaluations of diagnostic accuracy of a test based only on data from subjects with verified disease status are typically biased. This bias is known as verification bias. Various methods have been developed to adjust for verification bias in estimation of the ROC curve and its area for tests with binary or ordinal or continuous results. For the ROC surface and its volume, verification bias correction methods exist for tests with ordinal responses, but not for continuous tests. In this thesis, we propose several bias--corrected methods for estimating the ROC surface and the VUS of continuous diagnostic tests in presence of verification bias. In particular, these methods are constructed based on imputation and re--weighting techniques, and work well when the missingness mechanism of the true disease status is missing at random or missing not at random. The asymptotic behaviors of the estimators are also studied. To illustrate how to use the methods in real applications, two datasets dealing with epithelial ovarian cancer are considered. To support researchers in carrying out the ROC surface analysis in presence of verification bias, an R package and the corresponding Shiny web application have been created

Nonparametric estimation of ROC surfaces under verification bias: supplementary material.

none3noSupplementary Material for paper: Nonparametric estimation of ROC surfaces under verification bias published on REVSTAT STATISTICAL JOURNAL, 2020, 18, 697 - 720noneTO, DUC KHANH; Chiogna Monica; Adimari GianfrancoTO, DUC KHANH; Chiogna Monica; Adimari Gianfranc

ROC estimation and threshold selection criteria in three-class classification problems for clustered data

Statistical evaluation of diagnostic tests, and, more generally, of biomarkers, is a constantly developing field, in which complexity of the assessment increases with complexity of the design under which data are collected. One particularly prevalent type of data is clustered data, where individual units are naturally nested into clusters. In these cases, bias can arise from omission, in the evaluation process, of cluster-level effects and/or individual covariates. Focussing on the three-class case and for continuous-valued diagnostic tests, we investigate how to exploit the clustered structure of data within a linear-mixed model approach, both when the assumption of normality holds and when it does not. We provide a method for estimation of covariate-specific ROC surfaces and discuss methods for the choice of optimal thresholds, proposing three possible estimators. A proof of consistency and asymptotic normality of the proposed threshold estimators is given. All considered methods are evaluated by extensive simulation experiments. As an application, we study the use of the Lysosomal Associated Membrane Protein Family Member 5 (Lamp5) gene expression as biomarker to distinguish among three types of glutamatergic neurons