Statistical evaluation of diagnostic test under verification bias
Principal Investigator
Name
Khanh To Duc
Degrees
Ph.D
Institution
Department of Statistical Sciences, University of Padova, Padova, Italy
Position Title
Postdoctoral fellow
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-337
Initial CDAS Request Approval
Jan 18, 2018
Title
Statistical evaluation of diagnostic test under verification bias
Summary
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. In this project, we develop several bias–corrected methods for estimating the ROC surface and the VUS of continuous diagnostic tests in presence of verification bias.
Aims
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, some real datasets 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.
Collaborators
Monica Chigona and Gianfranco Adimari, Department of Statistical Sciences, University of Padova, Padova, Italy.