Using advanced data methods to compare cancer treatments across hospitals
Causal Machine Learning in Cancer Survival by Integrating Multiple High-Dimensional Observational Studies
['FUNDING_R01'] · UNIVERSITY OF MICHIGAN AT ANN ARBOR · NIH-11182466
We use medical records from many hospitals to learn which cancer treatments help people live longer.
Quick facts
| Phase | ['FUNDING_R01'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | UNIVERSITY OF MICHIGAN AT ANN ARBOR (nih funded) |
| Locations | 1 site (ANN ARBOR, UNITED STATES) |
| Trial ID | NIH-11182466 on ClinicalTrials.gov |
What this research studies
Researchers are combining large lung cancer databases from major hospitals and an international consortium to study survival after different treatments. They will develop and apply new causal machine-learning methods that handle differences in patient mixes, treatment practices, and complex high-dimensional data. By integrating multiple observational cohorts, the team aims to make treatment comparisons and survival predictions that hold up across different clinical settings. The work focuses on lung cancer patient records and statistical tools rather than testing a new drug or procedure on volunteers.
Who could benefit from this research
Good fit: People with lung cancer whose clinical records are part of participating centers or included in the Boston Lung Cancer Survival Cohort or the International Lung Cancer Consortium are the ideal data contributors for this work.
Not a fit: Patients with cancers other than lung cancer or those without available clinical records at the participating sites are unlikely to directly benefit from this project.
Why it matters
Potential benefit: If successful, this could produce more reliable information about which cancer treatments improve survival across different hospitals and patient groups.
How similar studies have performed: Prior observational and causal-inference studies have helped clarify treatment effects, but applying integrative causal machine-learning across many high-dimensional cancer cohorts is a newer and less-tested approach.
Where this research is happening
ANN ARBOR, UNITED STATES
- UNIVERSITY OF MICHIGAN AT ANN ARBOR — ANN ARBOR, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: LI, YI — UNIVERSITY OF MICHIGAN AT ANN ARBOR
- Study coordinator: LI, YI
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.
Conditions: Cancer Treatment, Cancers, Dana-Farber Cancer Institute