Real-world evidence
Longitudinal medication and health data help us study treatment use and outcomes in routine care.
CausalRx4AD
Studying existing medicines for Alzheimer’s disease and related dementias
We use real-world, genetic, and neuroimaging data to evaluate drug repurposing candidates.
Our work emphasizes careful study design, transparent analysis, and validation across data sources when possible.
Research information only; findings are not treatment recommendations.
About
A focused research program in ADRD drug repurposing
Drug repurposing examines whether medicines already used for other conditions may have potential for Alzheimer’s disease and related dementias. CausalRx4AD develops and applies methods to evaluate these questions with observational health data.
Multimodal data
Genetic and neuroimaging information can complement clinical outcomes and support interpretation.
Validation
We examine consistency across outcomes and data sources before prioritizing a candidate for further study.
Project
Current projects
Projects will be added as analyses begin and results become ready to share.
Active project
Using Neuroimaging Data to Identify Drug Repurposing Candidates for ADRD
This project integrates real-world, genetic, and neuroimaging data to evaluate existing medicines as potential drug repurposing candidates for Alzheimer’s disease and related dementias.
01
Assess candidate medicines using UK Biobank data.
02
Evaluate whether findings can be reproduced using NACC data.
Real-world dataMedication use and longitudinal health outcomes
GeneticsRisk and potential treatment-effect heterogeneity
NeuroimagingBrain structure and earlier markers of disease change
Causal methodsDesign, bias assessment, and validation
Research Reports
Selected findings and ongoing analyses
Reports are added when results are published or otherwise ready to share.
2026 · Alzheimer’s & Dementia
Ongoing
Statins and dementia in UK Biobank
A target trial emulation integrating primary-care and genetic data to assess average and heterogeneous treatment effects.
Read the report 2025 · American Journal of EpidemiologyDementia identification in Medicare claims
Algorithms for incident and prevalent dementia validated against syndromic classifications from the ARIC Study.
Read the reportNeuroimaging analyses for drug repurposing
Analyses evaluating whether treatment signals are consistent with changes in brain structure.
In progressResource
Methods and research resources
Selected methods, code, and data resources supporting the program.
Methods and code
Iterative Causal Forest
Methods for identifying interpretable treatment-effect heterogeneity in real-world data.
Methods and code
High-dimensional Iterative Causal Forest
A high-dimensional extension for subgroup identification using diagnosis, procedure, and medication codes.
Data resources
ADRD research data
Current projects use complementary clinical, genetic, and neuroimaging information.
Team
Project leadership
CausalRx4AD is led by Tiansheng (Tian) Wang, PharmD, PhD, Assistant Professor in the Department of Pharmaceutical Health Outcomes and Policy at the University of Houston College of Pharmacy.
The work brings together expertise in pharmacoepidemiology, causal inference, aging, neurology, genetics, neuroimaging, and data science.
News
Project updates
Brief updates on project support, publications, and public research outputs.
Alzheimer’s Association New Investigator Award
The award supports the project “Using Neuroimaging Data to Identify Drug Repurposing Candidates for ADRD.”