Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Department of Internal Medicine, The University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, USA.
Stat Methods Med Res. 2024 Aug;33(8):1424-1436. doi: 10.1177/09622802241262521. Epub 2024 Jul 25.
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.
本研究旨在探讨生物标志物在预测不同亚组患者后续时间事件结局方面的判别性能的异质性。虽然时间依赖性接收者操作特征曲线下面积（AUC）常用于评估生物标志物性能，但部分时间依赖性 AUC（PAUC）提供的信息通常更适用于人群筛查和诊断测试。为了实现这一目标，我们提出了一个针对 PAUC 的回归模型，并为离散和连续协变量开发了两种不同的估计程序，采用伪部分似然方法。通过模拟研究来评估这些程序在各种场景下的性能。我们将模型和推断程序应用于阿尔茨海默病神经影像学倡议数据集，以评估基于患者特征的生物标志物在早期阿尔茨海默病诊断方面的判别性能的潜在异质性。
