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

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2021 · $361,723

## Abstract

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.

## Key facts

- **NIH application ID:** 10102237
- **Project number:** 5R01DK117208-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** WENSHENG GUO
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $361,723
- **Award type:** 5
- **Project period:** 2019-02-15 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10102237

## Citation

> US National Institutes of Health, RePORTER application 10102237, Semi-Parametric Subgroup Analysis for Longitudinal Data with Applications to Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Study (5R01DK117208-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10102237. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*