Faculty, Staff and Student Publications
Publication Date
12-31-2024
Journal
Biostatistics
DOI
10.1093/biostatistics/kxae036
PMID
39255368
PMCID
PMC11823260
PubMedCentral® Posted Date
9-10-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Dynamic prediction models capable of retaining accuracy by evolving over time could play a significant role for monitoring disease progression in clinical practice. In biomedical studies with long-term follow up, participants are often monitored through periodic clinical visits with repeat measurements until an occurrence of the event of interest (e.g. disease onset) or the study end. Acknowledging the dynamic nature of disease risk and clinical information contained in the longitudinal markers, we propose an innovative concordance-assisted learning algorithm to derive a real-time risk stratification score. The proposed approach bypasses the need to fit regression models, such as joint models of the longitudinal markers and time-to-event outcome, and hence enjoys the desirable property of model robustness. Simulation studies confirmed that the proposed method has satisfactory performance in dynamically monitoring the risk of developing disease and differentiating high-risk and low-risk population over time. We apply the proposed method to the Alzheimer's Disease Neuroimaging Initiative data and develop a dynamic risk score of Alzheimer's Disease for patients with mild cognitive impairment using multiple longitudinal markers and baseline prognostic factors.
Keywords
Humans, Alzheimer Disease, Risk Assessment, Disease Progression, Cognitive Dysfunction, Models, Statistical, Algorithms, Longitudinal Studies, Neuroimaging, Machine Learning, Alzheimer’s disease, concordance-assisted learning, dynamic prediction, longitudinal markers, risk stratification
Published Open-Access
yes
Recommended Citation
Li, Wen; Li, Ruosha; Feng, Ziding; et al., "Dynamic and Concordance-Assisted Learning for Risk Stratification With Application to Alzheimer’s Disease" (2024). Faculty, Staff and Student Publications. 1196.
https://digitalcommons.library.tmc.edu/uthsph_docs/1196