Year: 2026
Type: article
Abstract: Covariate-specific and time-dependent area-under-curve (AUC) is often used to evaluate the discriminative performance of biomarkers with time-to-event outcomes, particularly when certain covariates influence biomarkers' accuracy. In biomarker research, despite extensive efforts, missing data remain unavoidable, with nonignorable missingness posing significant challenges. This article focuses on estimating the impact of covariates on time-dependent AUC in the presence of nonignorable missing biomarkers. Assuming a parametric model on the missing probability, we leverage instrumental variables to address the identifiable issue and estimate the unknown parameters. We integrate the inverse probability weighting approach into the score equation of a pseudo partial likelihood for estimation and inference. Additionally, we establish the asymptotic properties of the proposed estimators. Through simulation studies, we evaluate finite sample performance of the proposed estimators, and apply the method to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funders: U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Cancer Prevention and Research Institute of Texas, Biogen, University of Southern California, F. Hoffmann-La Roche, Meso Scale Diagnostics, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research, National Cancer Institute
Awards: G1012222616, G1298807586, G1554142629, G1593117279, G1650819308, G1880935189, Center for Clinical and Translational Sciences, G2100670906, G2152643862, G2189010064, G2340207221, G2423387114, G2471646051, G2522063444, G2835601961, G29951154, G3131374986, G3216574877, G3553156159, G3710456697, G3752451908, G3870774570, G3908963977, G3966393351, G4119534169, G43180419, G4475667458, G4501223628, G4800177743, G5207404921, G5209990037, G528601746, G5324093878, G5365730499, G5654639750, Alzheimer's Disease Neuroimaging Initiative, G5740349702, G5850682332, G613619302, G6173816828, G6210868510, G6371410416, Reducing Disparities in the Risk of Hepatocellular Cancer, G6412110892, G6447063303, G6719828067, G6792506723, G6899163935, G6948409098, G72415811, G7298697282, G764699806, G7799614282, G781725486, G8228018387, G8281378126, G839208290, G8852128998, G8895907669, G8897063270, G8957659042, The University of Texas MD Anderson Cancer Center SPORE in Ovarian Cancer, Statistical Methods for Integration of Multiple Data Sources toward Precision Cancer Medicine
OpenAlex ID: W7141220459