# Diversity Supplement: Developing a robust and efficient strategy for censored covariates to improve clinical trial design for neurodegenerative diseases

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2024 · $42,334

## Abstract

Project Summary
 Diseases of aging, like Alzheimer, Parkinson, and Huntington disease, are expected to affect 153 million individuals
worldwide by 2050.1, 2 Treatments to prevent or slow these diseases will significantly decrease the projected impact,
and modeling how disease symptoms worsen over time—the symptom trajectory—before and after a diagnosis can help
evaluate if a treatment can prevent or slow a disease. Yet modeling the symptom trajectory is not easy because these
diseases of aging progress slowly over decades, so studies that track symptoms often end before a diagnosis can be made.
This makes time to diagnosis right-censored (i.e., a patient will reach the criteria for a diagnosis sometime after the
last study visit, but exactly when is unknown), leaving researchers with the challenge of trying to model the symptom
trajectory without full information about when diagnosis occurs.
 This challenge raises the question: How do we model the symptom trajectory as a function of a right-censored
covariate, time to diagnosis? Endeavoring to answer this question using “model-based methods,” which use models to
estimate the expected time to diagnosis and then predict the symptom trajectory, are convenient, but when the model
for time to diagnosis is wrong, so too is the estimated symptom trajectory. In contrast, a model-free strategy makes
it easy to estimate the symptom trajectory without bias, but no model-free strategy yet exists that is simultaneously
robust and predictive. This NIH supplement will provide technical training opportunities to Mr. Kyle Grosser, a doctoral
candidate of biostatistics at UNC-Chapel Hill, so that he may advance our proposed two-step approach—wherein we
estimate a patient’s time to diagnosis and then estimate their symptom trajectory—by adjusting for the possibility that
this estimated time to diagnosis is error-prone. We will advance the two-step approach using this error adjustment, first for
linear longitudinal models (Aim 1) and then for nonlinear longitudinal models (Aim 2). This supplement will also provide
mentoring and career development elements tailored to prepare Mr. Grosser for a future career in academic research.
These components include instruction in effective oral and written communication; guidance in grant development and
proposal writing; engagement with the faculty, staff, and patients affiliated with the UNC Huntington Disease Program;
and opportunities to present his research at scientific conferences and meetings.

## Key facts

- **NIH application ID:** 10986516
- **Project number:** 3R01NS131225-01S1
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Tanya Pamela Garcia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $42,334
- **Award type:** 3
- **Project period:** 2023-06-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10986516

## Citation

> US National Institutes of Health, RePORTER application 10986516, Diversity Supplement: Developing a robust and efficient strategy for censored covariates to improve clinical trial design for neurodegenerative diseases (3R01NS131225-01S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10986516. Licensed CC0.

---

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