Robust and flexible inference for the covariate-specific ROC curve

Vanda Inacio de Carvalho, Vanda M. Lourenco, Miguel de Carvalho, Richard A Parker, Vincent J Gnanapragasam

Research output: Contribution to journalArticlepeer-review

Abstract

Diagnostic tests are of critical importance in health care and medical research. Motivated by the impact that atypical and outlying test outcomes might have on the assessment of the discriminatory ability of a diagnostic test, we develop a flexible and robust model for conducting inference about the covariate-specific receiver operating characteristic (ROC) curve that safeguards against outlying test results while also accommodating for possible nonlinear effects of the covariates. Specifically, we postulate a location-scale additive regression model for the test outcomes in both the diseased and nondiseased populations, combining additive cubic B-splines and M-estimation for the regression function, while the residuals are estimated via a weighted empirical distribution function. The results of the simulation study show that our approach successfully recovers the true covariate-specific ROC curve and corresponding area under the curve on a variety of conceivable test outcomes contamination scenarios. Our method is applied to a dataset derived from a prostate cancer study where we seek to assess the ability of the Prostate Health Index to discriminate between men with and without Gleason 7 or above prostate cancer, and if and how such discriminatory capacity changes with age.
Original languageEnglish
Pages (from-to)5779-5795
JournalSTATISTICS IN MEDICINE
Volume40
Issue number26
Early online date1 Sept 2021
DOIs
Publication statusPublished - 20 Nov 2021

Fingerprint

Dive into the research topics of 'Robust and flexible inference for the covariate-specific ROC curve'. Together they form a unique fingerprint.

Cite this