基于多元泛函主成分分析的异步纵向数据半参数混合回归。

IF 1.8 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Ruihan Lu, Yehua Li, Weixin Yao
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引用次数: 0

摘要

更年期的过渡阶段引起荷尔蒙的显著波动,对妇女的长期健康产生深远的影响。在一项关于中年及以后女性健康的广泛纵向调查中,被称为全国女性健康研究(SWAN),与其他容易出错的协变量(如身体和心血管测量)相比,激素生物标志物按照异步时间表被反复评估。我们采用半参数混合回归模型对SWAN数据进行了亚组分析,这使我们能够探索激素反应与其他时变或定常协变量之间的关系如何在亚组中变化。为了解决异步调度和测量误差带来的挑战,我们将时变协变量轨迹建模为具有降阶karhunen - losamuve展开的功能数据,其中样条用于捕获均值和特征函数。将潜在子群隶属度和功能主成分(FPC)分数作为缺失数据,我们提出了一种期望最大化算法来有效拟合联合模型,将激素反应的混合回归和异步时变协变量的FPC模型相结合。此外,我们还探索了数据驱动的方法来确定总体中子组的最佳数量。通过对SWAN数据的综合分析,我们揭示了老龄化女性人口中一个关键的亚群结构,揭示了更年期女性之间的重要区别和模式。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Semiparametric mixture regression for asynchronous longitudinal data using multivariate functional principal component analysis.

The transitional phase of menopause induces significant hormonal fluctuations, exerting a profound influence on the long-term well-being of women. In an extensive longitudinal investigation of women's health during mid-life and beyond, known as the Study of Women's Health Across the Nation (SWAN), hormonal biomarkers are repeatedly assessed, following an asynchronous schedule compared to other error-prone covariates, such as physical and cardiovascular measurements. We conduct a subgroup analysis of the SWAN data employing a semiparametric mixture regression model, which allows us to explore how the relationship between hormonal responses and other time-varying or time-invariant covariates varies across subgroups. To address the challenges posed by asynchronous scheduling and measurement errors, we model the time-varying covariate trajectories as functional data with reduced-rank Karhunen-Loéve expansions, where splines are employed to capture the mean and eigenfunctions. Treating the latent subgroup membership and the functional principal component (FPC) scores as missing data, we propose an Expectation-Maximization algorithm to effectively fit the joint model, combining the mixture regression for the hormonal response and the FPC model for the asynchronous, time-varying covariates. In addition, we explore data-driven methods to determine the optimal number of subgroups within the population. Through our comprehensive analysis of the SWAN data, we unveil a crucial subgroup structure within the aging female population, shedding light on important distinctions and patterns among women undergoing menopause.

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来源期刊
Biostatistics
Biostatistics 生物-数学与计算生物学
CiteScore
5.10
自引率
4.80%
发文量
45
审稿时长
6-12 weeks
期刊介绍: Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public''s health.
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