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

点击翻译按钮获取中文摘要

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.