ABSTRACT

Joint modeling analysis of longitudinal and time-to-event data has been an active area of statistical methodological study and biomedical research, but the majority of them are based on mean-regression. Quantile regression (QR) can characterize the entire conditional distribution of the outcome variable, and may be more robust to outliers/heavy tails and misspecification of error distribution. Additionally, a parametric specification may be insufficient and inflexible to capture the complicated longitudinal pattern of biomarkers. Thus, this study proposes novel QR-based partially linear mixed-effects joint models with three components (QR-based longitudinal response, longitudinal covariate, and time-to-event processes), and applies to Multicenter AIDS Cohort Study (MACS). Many common data features, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution, are considered to obtain reliable parameter estimates. Many interesting findings are discovered by the complicated joint models under Bayesian inference framework. Simulation studies are also implemented to assess the performance of the proposed joint models under different scenarios.

摘要

纵向数据和事件时间数据的联合建模分析一直是统计方法论研究和生物医学研究的活跃领域，但其中大多数是基于均值回归的。分位数回归（QR）可以表征结果变量的整个条件分布，并且对于异常值/重尾和误差分布的错误指定更为健壮。另外，参数规范可能不足以且不够灵活，无法捕获生物标志物的复杂纵向模式。因此，本研究提出了具有三个组成部分（基于QR的纵向响应，纵向协变量和事件发生时间过程）的基于QR的新颖的部分线性混合效应联合模型，并应用于多中心艾滋病队列研究（MACS）。许多常见的数据功能 包括由于检测限制而导致的左删截，协变量测量误差和不对称分布，均被视为获得可靠的参数估计值。在贝叶斯推理框架下，复杂的联合模型发现了许多有趣的发现。还进行了仿真研究，以评估所提出的联合模型在不同情况下的性能。