Semi-Parametric Subgroup Analysis for Longitudinal Data with Applications to Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Study

纵向数据的半参数亚组分析及其在慢性盆腔疼痛 (MAPP) 研究的多学科方法中的应用

• 批准号: 10348142
• 负责人: WENSHENG GUO
• 金额: $ 36.17万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-15 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10348142
• 关键词: Algorithms; Biological Markers; Classification; Data; Data Coordinating Center; Development; Dimensions; Ensure; Factor Analysis; Funding; Future; Goals; Intervention; Investigation; Lead; Longitudinal cohort study; Medical; Methods; Modeling; Monitor; National Institute of Diabetes and Digestive and Kidney Diseases; Outcome; Pain; Pathologic; Patients; Performance; Population; Preventive treatment; Public Domains; Recording of previous events; Research; Risk Factors; Sampling; Statistical Methods; Structure; Subgroup; Symptoms; Time; Validation; base; chronic pelvic pain; classification algorithm; clinically relevant; improved; interdisciplinary approach; longitudinal analysis; microbiome; neuroimaging; novel; predictive modeling; semiparametric; simulation; software development; statistics; time use; urinary; urologic chronic pelvic pain syndrome; user friendly software; working group

PROJECT SUMMARY The goal of this project is to develop novel statistical methods to cluster longitudinal/functional trajectories into subgroups, and to develop predictive models for cluster membership using both baseline and time-varying covariates. The proposed methods are motivated by, and will be applied to, the data collected in the NIDDK-funded Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network. This is an ongoing longitudinal cohort study that collects longitudinal urological chronic pelvic pain syndrome (UCPPS) symptom data, together with many other biomarkers, neuroimaging data and microbiome data. The goal of the study is identify risk factors that can predict whether the future UCPPS symptoms for a specific patient will either worsen or improve, to understand the underlying pathological mechanisms and to develop preventive treatments. We will first develop semi- parametric classification and clustering methods for longitudinal/functional data that will take into account both mean trajectories and time-varying variabilities in the clustering. We will then extend the methods to multivariate functional settings, in which we will simultaneously perform longitudinal factor analysis that reduces all the longitudinal symptoms into smaller dimensional factors, and cluster the subjects based on all the underlying factors. The third specific aim will develop time-varying classification and clustering methods. We also propose an online monitoring algorithm that will incorporate the existing population information in detecting the switching of a new subject based on his cumulative history and time-varying risk factors. This hopefully could lead to early medical interventions. All the proposed methods will be accompanied with user-friendly software packages, and will be applied to the data collected from the ongoing MAPP Research Network Studies.

项目总结 该项目的目标是开发新的统计方法，将纵向/功能轨迹归类为 利用基准协变量和时变协变量建立集群成员资格的预测模型。 建议的方法受到NIDDK基金收集的数据的启发，并将应用于这些数据 慢性盆腔疼痛研究的多学科方法(MAPP)研究网络。这是一个正在进行的 收集纵向泌尿系慢性盆腔疼痛综合征(UCPPS)症状数据的纵向队列研究， 与许多其他生物标志物、神经成像数据和微生物组数据一起。研究的目标是识别风险 可以预测特定患者未来UCPPS症状是恶化还是改善的因素，以 了解潜在的病理机制并开发预防性治疗方法。我们将首先开发半 纵向/功能性数据的参数分类和聚类方法，该方法将考虑两者的平均值 聚类中的轨迹和时变变化。然后我们将把这些方法扩展到多元泛函 设置，在该设置中，我们将同时执行纵向因素分析，以减少所有纵向 将症状分解为较小维度的因素，并根据所有潜在因素对受试者进行分类。第三 具体目标是开发时变分类和聚类方法。我们还提出了在线监测 一种算法，该算法将结合现有的人口信息来检测新主题的切换 他的累积病史和时变的风险因素。这有望导致早期的医疗干预。所有的 建议的方法将与用户友好的软件包一起使用，并将应用于收集的数据 来自正在进行的Mapp研究网络研究。