# Harmonizing multiple clinical trials for Alzheimer's disease to investigate differential responses to treatment via federated counterfactual learning

> **NIH NIH R01** · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · 2024 · $679,268

## Abstract

Drug development for treating Alzheimer's disease (AD) has been challenging and expensive.
Drug failures are very likely due, in large part, to the differential responses of patients to
different treatments. Some subsets of patients have treatment moderators and respond
differently. Identifying such responsive subsets has been challenging due to limited sample size
in one clinical trial or may be beyond the scope of the ad-hoc analyses in individual clinical
trials, considering the complexity of AD. Another important subset of patients are rapid
progressors, who have faster rates of cognitive decline in a defined period and may respond
differently to treatments than other AD patients. Predicting the rapid progressors and their
differential responses is very challenging. Machine learning prediction has been no better than
random guesses due to volatility of cognitive scores and insufficiency of comprehensive and
fine-grained longitudinal clinical data. Pooling patient-level data from multiple clinical trials data
may address the above challenges by increasing sample size and obtaining a better
coverage/representation of the patient population. However, many clinical trials data are stored
in distributed data access servers, and data use agreements often prohibit exporting the patient-
level data out of the local servers. We aim to address the challenges via advanced informatics
tools using AI/ML models. We will develop privacy-preserving federated models to harmonize
local counterfactual effect estimation models into a global model without exchanging patient-
level data. Aim 1 focuses on developing a federated subgrouping model based on differential
responses. Aim 2 focuses on developing a federated counterfactual regression model using
deep learning to predict rapid progressors and their differential responses. Aim 3 focuses on
verifying and refining the subgroups prediction using real-world observation in nation-wide
consortium data. If successful, this project will contribute to identifying patient subgroups that
respond differently, which will result in smaller, less expensive, and more targeted AD clinical
trials that expose fewer patients to experimental medications to which they are unlikely to
respond.

## Key facts

- **NIH application ID:** 10909237
- **Project number:** 5R01AG082721-02
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
- **Principal Investigator:** Xiaoqian Jiang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $679,268
- **Award type:** 5
- **Project period:** 2023-09-01 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10909237

## Citation

> US National Institutes of Health, RePORTER application 10909237, Harmonizing multiple clinical trials for Alzheimer's disease to investigate differential responses to treatment via federated counterfactual learning (5R01AG082721-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10909237. Licensed CC0.

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