# Innovative precision medicine methods in subgroup identification for Alzheimer's disease

> **NIH NIH R21** · WASHINGTON UNIVERSITY · 2024 · $194,375

## Abstract

is a major and rapidly increasing public health concern: over 30 million individuals
worldwide suffer from AD, which is projected to quadruple by 2050. AD has been reported to be the third leading
cause of death in the US. With this impending global public health crisis, treatments that prevent onset or slow
progression of AD are urgently needed but rarely available until the recent accelerated approval for aducenumab.
Therefore, it is of great interest to identify subpopulations which benefit most from a medication when the overall
treatment effect is minimum or not clinically meaningful. If such subpopulations can be identified, some of the
treatments from the negative trials can be proven to at least help a portion of the AD population. In this proposal
we will employ non-parametric interaction tree (IT)-based methods on mixed models for repeated measures
(MMRM) and regression-based methods to identify such subpopulations. IT for MMRM builds on the assessment
of the treatment-by-covariates interactions and can automatically seek subgroups of individuals in whom the
treatment shows heterogeneous effects. We also explore a new and more attractive fusion penalty approach for
final tree determination without any prior knowledge of grouping information. The regression-based methods aim
to identify subpopulations who will benefit from AD treatment based on their characteristics, which is very flexible
to make individualized treatment selection. Finally, we will develop and disseminate a user-friendly statistical
software package that will enable researchers to implement these methods with ease. Our extensions will better
capture individual heterogeneity in disease progression and facilitate evidence-based precision medicine in
future AD studies and other research areas.

## Key facts

- **NIH application ID:** 10912813
- **Project number:** 5R21AG084054-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Lei Liu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $194,375
- **Award type:** 5
- **Project period:** 2023-09-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10912813

## Citation

> US National Institutes of Health, RePORTER application 10912813, Innovative precision medicine methods in subgroup identification for Alzheimer's disease (5R21AG084054-02). Retrieved via AI Analytics 2026-05-31 from https://api.ai-analytics.org/grant/nih/10912813. Licensed CC0.

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