Finding when conditions begin between clinic visits
Semiparametric Regression Analysis of Interval-Censored Data in Current Cohort Studies
This project creates better statistical tools to pinpoint when infections or chronic conditions start for people who have periodic medical exams.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Michigan at Ann Arbor NIH-funded |
| Lab location | 1 site (Ann Arbor, United States) |
| Project ID | NIH-11253938 on NIH RePORTER |
What this research studies
The team will develop semiparametric regression models to handle 'interval-censored' times when a disease onset is only known to occur between two clinic visits. They will extend these models to study how hidden onset times influence later outcomes and to model multi-stage disease progression with random effects. The project includes graphical and numerical checks to make sure the models fit real cohort data and relaxes common assumptions like proportional hazards to improve accuracy. The methods will be implemented as algorithms that can be applied to existing cohort studies across conditions such as COVID-19, HIV, diabetes, COPD, cancer, and dementia.
Who could benefit from this research
Good fit: People enrolled in long-term cohort studies or who receive routine periodic exams (for example for COVID-19, HIV, diabetes, COPD, cancer, or dementia monitoring) would be the relevant participants whose data this work uses.
Not a fit: Individuals not enrolled in cohort studies or without regular follow-up exams are unlikely to be directly affected by this project's methods.
Why it matters
Potential benefit: If successful, these tools could help researchers and clinicians more accurately determine when diseases begin and progress, improving study findings and potentially informing better timing of care.
How similar studies have performed: Existing methods for interval-censored data exist but can be unreliable, so this work builds on prior approaches while developing more flexible and robust solutions.
Where this research is happening
Ann Arbor, United States
- University of Michigan at Ann Arbor — Ann Arbor, United States (Active)
Researchers
- Principal investigator: Zeng, Donglin — University of Michigan at Ann Arbor
- Study coordinator: Zeng, Donglin
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.