# Statistical disease modeling and clinimetrics to prepare for preventive trials in huntington disease

> **NIH NIH U01** · UNIVERSITY OF WISCONSIN-MADISON · 2021 · $548,286

## Abstract

Project Summary
Huntington disease (HD) is a fatal, costly, autosomal dominant neurodegenerative disease with no available
disease-modifying treatments. While genetic testing can identify the presence of the mutant HTT gene, there
are no markers to document the initiation of pathological changes or demarcate when preventive interventions
are optimal to maximize quality of life. While clinical trials to slow progression and delay or prevent onset are
beginning, methods to assess their efficacy are lacking. The proposed research will fill these gaps in our ability
to design preventive clinical trials for HD. First, we will use the legacy PREDICT data to develop statistical
models of onset, progression rate and phenotypic heterogeneity and then we will recruit 250 participants to
complete the field's best Clinical Outcome Assessments (COAs) for premanifest HD and subject them to
clinimetric tests of reliability, validity and responsiveness so future preventive trialists can utilize the information
to design well-powered and efficient trials to delay the onset or slow the progression of HD

## Key facts

- **NIH application ID:** 10213850
- **Project number:** 5U01NS103475-05
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Jane S Paulsen
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $548,286
- **Award type:** 5
- **Project period:** 2017-07-01 → 2023-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10213850

## Citation

> US National Institutes of Health, RePORTER application 10213850, Statistical disease modeling and clinimetrics to prepare for preventive trials in huntington disease (5U01NS103475-05). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10213850. Licensed CC0.

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