Better ways to predict breast cancer risk over time
Dynamic prediction incorporating time-varying covariates for the onset of breast cancer
This research aims to create new ways to predict a woman's breast cancer risk more accurately by looking at how her health factors change over many years.
Quick facts
| Grant type | R37 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Washington University NIH-funded |
| Lab location | 1 site (Saint Louis, United States) |
| Project ID | NIH-11045529 on NIH RePORTER |
What this research studies
We know that many factors can influence breast cancer risk, and these factors can change throughout a person's life. Current risk prediction tools often only look at a single snapshot of health information. This project develops advanced statistical models that consider how your health information, like BMI, changes over time. By understanding these changing patterns, we hope to offer a more personalized and precise estimate of your breast cancer risk. This could help doctors provide more tailored prevention strategies.
Who could benefit from this research
Good fit: This research is relevant to women concerned about their breast cancer risk, especially those with changing health factors over time.
Not a fit: Patients already diagnosed with breast cancer or those not at risk for the disease may not directly benefit from this risk prediction research.
Why it matters
Potential benefit: If successful, this work could lead to more accurate and personalized breast cancer risk predictions, helping individuals and their doctors make better decisions about prevention and screening.
How similar studies have performed: While traditional models exist, this approach is novel in its focus on dynamically incorporating how individual health factors change over time for improved prediction.
Where this research is happening
Saint Louis, United States
- Washington University — Saint Louis, United States (Active)
Researchers
- Principal investigator: Jiang, Shu — Washington University
- Study coordinator: Jiang, Shu
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.