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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\u2019s Disease Neuroimaging Initiative data set to evaluate potential heterogeneities in the discriminative performance of biomarkers for early Alzheimer\u2019s disease diagnosis based on patients\u2019 characteristics. <\/jats:p>","DOI":"10.1177\/09622802241262521","type":"journal-article","created":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T23:07:01Z","timestamp":1721948821000},"page":"1424-1436","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Analyzing heterogeneity in biomarker discriminative performance through partial time-dependent receiver operating characteristic curve modeling"],"prefix":"10.1177","volume":"33","author":[{"given":"Xinyang","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, 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