Statistical methods for joint modeling and dynamic predictions for clustered data

联合建模在捕获纵向和循环结果在右审查事件时间上的时间动态方面提供了很大的灵活性，并使用所有可用信息提供动态更新的风险预测。虽然联合建模和动态预测的统计方法广泛用于独立数据，但很少有方法用于聚类数据。我的研究计划的主要目标是开发和评估联合建模和动态预测的统计方法，并解决基于家庭的遗传研究中聚类数据分析所产生的统计挑战。提出的研究有以下具体目的：1。开发有效的统计方法，对聚类数据进行联合建模，包括随访期间收集的纵向、复发和生存结果，这些数据考虑了研究设计、多个终点事件、时间相关协变量和聚类内相关性；2.开发用于测量动态预测准确性的统计方法，该方法考虑了右审查、竞争事件和聚类内相关性，并使用模拟研究评估其性能；和3。将高通量基因组数据整合到联合模型中，以提高个性化的动态预测。在目标1中，我将为聚类数据开发各种类型的联合模型：1)在随访期间测量的生存、复发和纵向结果的三元联合模型，该模型结合了各种类型纵向数据的广义线性模型；2)考虑竞争事件和连续事件以及复发和纵向结果的多状态联合模型。此外，我计划解决联合建模中的两个重要问题：3)建模复杂的依赖结构和4)调整由采样方案引起的确定偏差。最后，我计划5)发展复合似然，以减少具有复杂关联结构和确定校正的联合建模的计算负担。在目标2中，我将通过纳入个体和家族事件史，对目标1中提出的联合模型进行动态预测，并根据接受者工作特征曲线（AUC）和Brier评分（BS）下的时间依赖面积评估其预测准确性。由于AUC和BS是针对独立数据导出的，我计划开发改进的AUC和BS估计器，以考虑聚类内相关性、审查和竞争事件。在目标3中，我将把下一代测序数据整合到联合模型中，以识别与感兴趣的事件和纵向结果相关的遗传多态性，以改进事件风险的动态预测。拟议的研究将改进风险估计和个人对多个事件的动态预测，纵向结果和高通量数据，在遗传学和健康科学中具有许多应用潜力。

Joint modeling offers great flexibility in capturing the temporal dynamics of longitudinal and recurrent outcomes on right-censored event times and provides dynamically updated risk prediction using all available information. Although statistical methods for joint modeling and dynamic prediction are widely available for independent data, few methods have been developed for clustered data. The main goals of my research program are to develop and evaluate statistical methods for joint modeling and dynamic predictions and address the statistical challenges arising from the analysis of clustered data in family-based genetic studies. The proposed research has the following specific aims: 1.Develop efficient statistical methods for joint modeling for clustered data including longitudinal, recurrent and survival outcomes collected during follow-up that account for study design, multiple terminal events, time-dependent covariates, and within-cluster correlation; 2.Develop statistical methods for measuring dynamic prediction accuracy that account for right-censoring, competing events, and within-cluster correlation and evaluate their performance using simulation studies; and 3.Integrate high-throughput genomic data into joint models to improve individualized dynamic predictions. In Aim 1, I will develop various types of joint models for clustered data: 1) trivariate joint models for survival, recurrent and longitudinal outcomes measured during follow-up that incorporate a generalized linear model for various types of longitudinal data and 2) multistate joint models that account for competing events and successive events along with recurrent and longitudinal outcomes. In addition, I plan to address two important issues in joint modeling: 3) modeling complex dependence structure and 4) adjusting ascertainment bias due to sampling schemes. Finally, I plan to 5) develop composite likelihoods to reduce computational burdens for joint modeling with complex correlation structure and ascertainment correction. In Aim 2, I will derive dynamic predictions for the joint models proposed in Aim 1 by incorporating individual and familial history of events and assess their predictive accuracy based on the time-dependent area under the receiver operating characteristic curve (AUC) and Brier scores (BS). As the AUC and BS were derived for independent data, I plan to develop modified AUC and BS estimators to account for within-cluster correlation, censoring and competing events. In Aim 3, I will integrate next generation sequencing data into joint models to identify genetic polymorphisms associated with the event(s) of interest and longitudinal outcomes to improve dynamic predictions of the event risk. The proposed research will improve risk estimation and individual's dynamic predictions accounting for multiple events, longitudinal outcomes and high-throughput data with the potential for a number of applications in genetics and health sciences.