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.
翻译:在流行病学和临床研究中,根据纵向剖面确定病人的苯型对了解该疾病的发展模式至关重要。目前研究的动机是加拿大出生组群研究《儿童科霍特研究》的数据。我们的目标是使用多种纵向呼吸特征将参与者分组,形成类似的纵向呼吸剖面,以便确定与临床有关的疾病苯型。为了适当说明这些纵向标记的不同结构和类型,我们提出了一个新的混合型(连续、离散和绝对)多变量长纵向数据组合联合模型。我们还开发了Markov链蒙特卡洛算法,以估计模型参数的后方分布。对儿童科霍特数据和模拟数据的分析得到介绍和讨论。我们的研究显示,拟议的模型是将多变量混合长纵向数据分组的有用分析工具。我们开发了一套R包BCCLong,以高效实施拟议的模型。