Faculty, Staff and Student Publications
Publication Date
8-1-2024
Journal
Statistical Methods in Medical Research
Abstract
This study investigates the heterogeneity of a biomarker's discriminative performance for predicting subsequent time-to-event outcomes across different patient subgroups. While the area under the curve (AUC) for the time-dependent receiver operating characteristic curve is commonly used to assess biomarker performance, the partial time-dependent AUC (PAUC) provides insights that are often more pertinent for population screening and diagnostic testing. To achieve this objective, we propose a regression model tailored for PAUC and develop two distinct estimation procedures for discrete and continuous covariates, employing a pseudo-partial likelihood method. Simulation studies are conducted to assess the performance of these procedures across various scenarios. We apply our model and inference procedure to the Alzheimer's Disease Neuroimaging Initiative data set to evaluate potential heterogeneities in the discriminative performance of biomarkers for early Alzheimer's disease diagnosis based on patients' characteristics.
Keywords
Humans, Alzheimer Disease, Biomarkers, ROC Curve, Area Under Curve, Models, Statistical, Likelihood Functions, Computer Simulation, Neuroimaging
DOI
10.1177/09622802241262521
PMID
39053568
PMCID
PMC11449645
PubMedCentral® Posted Date
7-25-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
