Zhang Jing, Ning Jing, Li Ruosha
Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, 1200 Pressler St, Houston, TX 77030, USA.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030, USA.
Stat Biosci. 2023 Jul;15(2):353-371. doi: 10.1007/s12561-023-09362-0. Epub 2023 Feb 2.
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.
生存结局的风险预测模型在医学研究中被广泛应用，以预测事件发生的未来风险。在许多临床研究中，生物标志物数据会随时间重复测量。为便于及时进行疾病预后评估和决策，人们开发了许多动态预测模型，并能实时生成预测结果。由于动态预测模型会根据新的测量数据随时间更新个体的风险预测，因此考察模型在不同测量时间和预测时间的表现通常很重要。在本文中，我们为动态预测模型提出了一种二维曲线下面积（AUC）度量，并开发了相关的估计和推断程序。估计程序在两种生物标志物测量计划下进行讨论：定期访视和不定期访视。通过最大化一个伪偏似然函数有效地估计模型参数。我们将所提出的方法应用于一项肾移植研究，以评估基于纵向生物标志物的动态预测模型对移植失败的区分性能。
