Better ways to compare treatments using complex health data
Novel and Rigorous Statistical Learning and Inference for Comparative Effectiveness Research with Complex Data
Developing new statistical tools to help compare treatment safety and effectiveness for people with diabetes and heart rhythm problems.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Rutgers, the State Univ of N.j. NIH-funded |
| Lab location | 1 site (Piscataway, United States) |
| Project ID | NIH-11181302 on NIH RePORTER |
What this research studies
Researchers are building automated, rigorous statistical methods that can use large, complex patient data to estimate how different treatments perform in the real world. The work focuses on handling confounding, instrumental variables, and many interacting patient factors so comparisons between drugs are more reliable. They will apply and test these tools using clinical datasets related to adult-onset diabetes and heart rhythm conditions like atrial fibrillation, and will share software and guidance with other teams. The aim is to help clinicians and patients make clearer choices when randomized trials are not available.
Who could benefit from this research
Good fit: Adults with type 2 (adult-onset) diabetes or atrial fibrillation — especially those using anticoagulant or diabetes medications — are the kinds of patients whose records and outcomes this work aims to clarify.
Not a fit: People with conditions unrelated to diabetes or heart rhythm disorders, or those without electronic health records or registry data, are unlikely to see direct benefit from this project.
Why it matters
Potential benefit: If successful, this could give patients and clinicians more reliable, clearer comparisons of treatment risks and benefits drawn from real-world medical records.
How similar studies have performed: Related causal-inference and machine-learning methods have improved some observational comparisons in the past, but this project extends and automates those approaches for much higher-dimensional and more complex data.
Where this research is happening
Piscataway, United States
- Rutgers, the State Univ of N.j. — Piscataway, United States (Active)
Researchers
- Principal investigator: Tan, Zhiqiang — Rutgers, the State Univ of N.j.
- Study coordinator: Tan, Zhiqiang
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page below.