Evaluating Dynamic Discrimination Performance of Risk Prediction Models for Survival Outcomes

Jing Zhang (), Jing Ning () and Ruosha Li ()
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Jing Zhang: The University of Texas Health Science Center at Houston
Jing Ning: The University of Texas MD Anderson Cancer Center
Ruosha Li: The University of Texas Health Science Center at Houston

Statistics in Biosciences, 2023, vol. 15, issue 2, No 3, 353-371

Abstract: 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 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12561-023-09362-0

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