ArticleStatistical methods in medical research2024
Analyzing heterogeneity in biomarker discriminative performance through partial time-dependent receiver operating characteristic curve modeling.
Article in Statistical methods in medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Addressing nonignorable missing data and heterogeneity in prognostic biomarker assessment.Statistical methods in medical research · 2026Article
- Single-Cell and Bulk Transcriptomic Integration Reveals a Stemness-Related Astrocyte Subpopulation for Prognostic Risk Stratification in Glioblastoma.Current medicinal chemistry · 2026Article
- Accounting for Competing Risks in the Assessment of Prognostic Biomarkers' Discriminative Accuracy.Statistics in biosciences · 2025Article
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Authors and funding
5 authors.
Funding
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.
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