论文标题
聚集混合型多元纵向数据的联合建模方法：适用于儿童队列研究
A Joint Modeling Approach for Clustering Mixed-Type Multivariate Longitudinal Data: Application to the CHILD Cohort Study
论文作者
论文摘要
在流行病学和临床研究中，基于纵向特征的患者表型对于理解疾病的发育模式至关重要。当前的研究是由加拿大出生队列研究（儿童队列研究）的数据激励的。我们的目标是使用多种纵向呼吸系统特征将参与者聚集到具有相似纵向呼吸系统的亚组中，以鉴定临床上相关的疾病表型。为了适当说明这些纵向标记的不同结构和类型，我们提出了一种新型的联合模型，用于聚集混合型（连续，离散和分类）多元纵向数据。我们还开发了马尔可夫链蒙特卡洛算法，以估计模型参数的后验分布。介绍并讨论了儿童队列数据和模拟数据的分析。我们的研究表明，所提出的模型是聚集多元混合型纵向数据的有用分析工具。我们开发了一个r bcclong，以有效地实现了提出的模型。
In epidemiological and clinical studies, identifying patients' phenotypes based on longitudinal profiles is critical to understanding the disease's developmental patterns. The current study was motivated by data from a Canadian birth cohort study, the CHILD Cohort Study. Our goal was to use multiple longitudinal respiratory traits to cluster the participants into subgroups with similar longitudinal respiratory profiles in order to identify clinically relevant disease phenotypes. To appropriately account for distinct structures and types of these longitudinal markers, we proposed a novel joint model for clustering mixed-type (continuous, discrete and categorical) multivariate longitudinal data. We also developed a Markov Chain Monte Carlo algorithm to estimate the posterior distribution of model parameters. Analysis of the CHILD Cohort data and simulated data were presented and discussed. Our study demonstrated that the proposed model serves as a useful analytical tool for clustering multivariate mixed-type longitudinal data. We developed an R package BCClong to implement the proposed model efficiently.
