Statistical methods for joint modeling and dynamic predictions for clustered data

聚类数据联合建模和动态预测的统计方法

• 批准号: RGPIN-2019-06549
• 负责人: Choi, YunHee
• 金额: $ 1.31万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714190
• 关键词: Statistical; methods; joint; modeling; dynamic

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.

联合建模在捕获右删失事件时间上的纵向和复发性结果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是广泛适用于独立的数据，很少有方法已被开发的集群数据。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，并解决在基于家族的遗传研究中聚类数据分析所带来的统计挑战。 拟议的研究有以下具体目标： 1.开发有效的统计方法，用于聚类数据的联合建模，包括随访期间收集的纵向、复发和生存结局，这些数据解释了研究设计、多个终末事件、时间依赖性协变量和群内相关性; 2.制定统计方法，衡量动态预测的准确性，考虑到右删失、竞争事件和集群内相关性，并使用模拟研究评估其性能; 3.将高通量基因组数据整合到联合模型中，以改善个性化的动态预测。 在目标1中，我将为聚类数据开发各种类型的联合模型：1）随访期间测量的生存、复发和纵向结局的三变量联合模型，其中包含各种类型纵向数据的广义线性模型; 2）多状态联合模型，其中考虑竞争事件和连续事件沿着复发和纵向结局。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）调整由于抽样方案的确定偏差。最后，我计划5）开发复合似然，以减少具有复杂相关结构和确定校正的联合建模的计算负担。 在目标2中，我将通过纳入个人和家族事件史来推导目标1中提出的联合模型的动态预测，并根据受试者工作特征曲线下面积（AUC）和Brier评分（BS）评估其预测准确性。由于AUC和BS是针对独立数据推导的，因此我计划开发修改的AUC和BS估计值，以解释群内相关性、删失和竞争事件。 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，从而改善事件风险的动态预测。 拟议的研究将改善风险估计和个人的动态预测，考虑多个事件，纵向结果和高通量数据，在遗传学和健康科学中有许多应用的潜力。