# 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 · $33,818

## 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 creates a unique statistical problem of modeling the symptom trajectory as a function of a right-
censored covariate, time to diagnosis. Tackling this problem with “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. Vazquez, a doctoral candidate of
biostatistics at UNC-Chapel Hill, so that he may develop a model-free strategy that is simultaneously robust and predictive
to improve both forecasting of expected symptom trajectories during clinical trials (Aim 1) and patient selection for those
trials (Aim 2). The supplement will also provide mentoring and career development components designed to position Mr.
Vazquez for a future career in academic research. These components include training in oral and written communication;
training in grant development and writing; interacting with faculty, staff, and patients of the UNC Huntington Disease
Program; presenting results at scientific meetings; and experiencing both sides of the peer review process.

## Key facts

- **NIH application ID:** 10986495
- **Project number:** 3R01NS131225-01S2
- **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:** $33,818
- **Award type:** 3
- **Project period:** 2023-06-01 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10986495

## Citation

> US National Institutes of Health, RePORTER application 10986495, Diversity Supplement: Developing a robust and efficient strategy for censored covariates to improve clinical trial design for neurodegenerative diseases (3R01NS131225-01S2). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10986495. Licensed CC0.

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