# Time-Dynamic Tree-Based Methods for Personalized Alzheimer's Disease Prediction

> **NIH NIH R03** · JOHNS HOPKINS UNIVERSITY · 2024 · $327,500

## Abstract

Project Summary (Abstract)
This two-year grant proposal responds to PAR-23-179, and its long-term goal is to advance the understanding
of biomarker in relation to preclinical Alzheimer’s Disease (AD). This proposal seeks to leverage interpretable
and flexible tree-based methods, clinically and biologically informed tree decision rules, longitudinal
biomarkers, and risk and protective factors, to develop novel statistical methods and construct tree-based
algorithms. The novel methods and tree-based algorithms will help identify time-dynamic and personalized
biomarker subgroups at high risk for cognitive decline due to AD and predict progression risks.
AD is a devastating disease affecting over 6 million people in the U.S. and has burdened the U.S. healthcare
system and caregivers with increases. Importantly, evidence suggests that the pathophysiological process
begins many years, if not decades, before the diagnosis of AD dementia, and recent findings demonstrate that
biomarker deterioration starts many years before cognitive decline due to AD. Identifying high-risk subgroups
based on biomarker information will expand the window of opportunity during which therapeutic intervention
may have the greatest potential for success.
Tree-based methods appear well-suited for producing clinically applicable decision rules that leverage
complicated interactions between different biomarkers, and between biomarkers and risk factors. But existing
tree-based methods are limited in clinical relevance, biological interpretability, and statistical inference, and
novel methods are needed. Meanwhile, the consortium of multiple longitudinal follow-up studies presents new
opportunities and challenges. The Preclinical AD Consortium (PAC) data comprises five studies that have
been collecting longitudinal clinical, cognitive, biomarker, and genetic data from individuals who were
cognitively normal when first enrolled and followed for many years. The large sample size and the breadth of
the merged and harmonized data create opportunities for more precise and personalized classification and risk
prediction, based on longitudinal biomarkers and risk and protective factors. These opportunities also create
challenges for method development to be time-dynamic and personalized.
Projects supported by this proposal will seek to develop novel tree-based statistical methods for subgroup
identification and risk prediction for cognitive decline due to AD, and construct classification and prediction
algorithms using the PAC data. Our research team will pursue these goals through two Specific Aims: (1)
establish a time-dynamic and personalized statistical classification and prediction framework using tree-based
methods; and (2) leverage the PAC data to construct classification and prediction algorithms for onset of AD-
related clinical symptoms. Successful completion of these Aims will produce novel tree-based methodologies
and generate tree-based classification and prediction algorithm...

## Key facts

- **NIH application ID:** 10890391
- **Project number:** 1R03AG083470-01A1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Yuxin Zhu
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $327,500
- **Award type:** 1
- **Project period:** 2024-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10890391

## Citation

> US National Institutes of Health, RePORTER application 10890391, Time-Dynamic Tree-Based Methods for Personalized Alzheimer's Disease Prediction (1R03AG083470-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10890391. Licensed CC0.

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