Faculty, Staff and Student Publications
Publication Date
7-1-2023
Journal
Statistics in Biosciences
Abstract
Risk prediction models for survival outcomes are widely applied in medical research to predict future risk for the occurrence of the event. In many clinical studies, the biomarker data are measured repeatedly over time. To facilitate timely disease prognosis and decision making, many dynamic prediction models have been developed and generate predictions on a real-time basis. As a dynamic prediction model updates an individual's risk prediction over time based on new measurements, it is often important to examine how well the model performs at different measurement times and prediction times. In this article, we propose a two-dimensional area under curve (AUC) measure for dynamic prediction models and develop associated estimation and inference procedures. The estimation procedures are discussed under two types of biomarker measurement schedules: regular visits and irregular visits. The model parameters are estimated effectively by maximizing a pseudo-partial likelihood function. We apply the proposed method to a renal transplantation study to evaluate the discrimination performance of dynamic prediction models based on longitudinal biomarkers for graft failure.
Keywords
Dynamic prediction, Longitudinal biomarkers, Partly conditional survival model, Time-dependent AUC, Validation, Predictive discrimination
DOI
10.1007/s12561-023-09362-0
PMID
37691982
PMCID
PMC10483238
PubMedCentral® Posted Date
7-1-2024
PubMedCentral® Full Text Version
Author MSS
Published Open-Access
yes