Combining patient groups to find cancer risk patterns and reduce disparities
An integrated, multi-cohort approach for cancer health disparities and risk assessment
This project uses data from many different groups of cancer patients to build better tools that predict who is at higher risk and who might avoid unnecessary treatments.
Quick facts
| Grant type | U01 cooperative agreement |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Auburn University at Auburn NIH-funded |
| Lab location | 1 site (Auburn, UNITED STATES) |
| Project ID | NIH-11263297 on NIH RePORTER |
What this research studies
From a patient's point of view, researchers will pool medical records, tumor data, and outcomes from many hospitals and regions to spot groups that experience worse cancer outcomes or unequal care. They will apply advanced Bayesian statistical models to predict which patients are likely to benefit from more aggressive treatment and which may be safely spared overtreatment. The effort emphasizes including diverse populations to uncover risk factors linked to race, location, or socioeconomic status. Results aim to produce prediction tools that work better across real-world, varied patient groups.
Who could benefit from this research
Good fit: Ideal candidates are people represented in the contributing cancer cohorts—for example patients with breast cancer, non-small cell lung cancer, or other common cancers—especially from underrepresented demographic or geographic groups with available clinical and outcome data.
Not a fit: People without accessible medical records, those with cancer types not included in the pooled cohorts, or patients from regions not covered by the contributing datasets may not directly benefit from this project.
Why it matters
Potential benefit: If successful, this work could help doctors match treatments to the patients most likely to benefit and reduce unnecessary therapies for low-risk people.
How similar studies have performed: Previous single-cohort risk models have helped in specific settings, but this integrated multi-cohort Bayesian approach is relatively new and aims to improve prediction across diverse populations.
Where this research is happening
Auburn, UNITED STATES
- Auburn University at Auburn — Auburn, United States (Active)
Researchers
- Principal investigator: Nguyen, Tin C — Auburn University at Auburn
- Study coordinator: Nguyen, Tin C
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.