Better statistical tools for HIV lab results and outcomes
Statistical Methods for Ordinal Variables in HIV/AIDS Studies
['FUNDING_R01'] · VANDERBILT UNIVERSITY MEDICAL CENTER · NIH-11240320
Researchers are building smarter statistical methods to help make clearer sense of HIV lab tests and patient outcomes, even when results are skewed, categorical, or partly unmeasurable.
Quick facts
| Phase | ['FUNDING_R01'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | VANDERBILT UNIVERSITY MEDICAL CENTER (nih funded) |
| Locations | 1 site (NASHVILLE, UNITED STATES) |
| Trial ID | NIH-11240320 on ClinicalTrials.gov |
What this research studies
This project develops new statistical approaches to handle HIV-related measurements that are skewed, reported as categories, or have detection limits. The team extends cumulative probability models and adds modern techniques like penalized regression (ridge, lasso, elastic net) and tree-based methods (random forests, boosting) so predictions can flexibly include many patient factors. They will also create rank-based methods to estimate robust treatment effect measures from observational data, such as quantile treatment effects and probabilistic index measures. Methods will be checked using simulations and real HIV datasets to ensure they perform well in practice.
Who could benefit from this research
Good fit: People living with HIV whose medical records, lab tests (e.g., CD4/CD8 counts), or other clinical data are included in research or observational studies would be the indirect candidates to benefit from this work.
Not a fit: Patients without HIV or those whose data are not part of research datasets are unlikely to be affected directly by these methods.
Why it matters
Potential benefit: If successful, these methods could give researchers more reliable answers about how treatments and other factors affect people with HIV when data are messy or nonstandard.
How similar studies have performed: Related rank-based and semiparametric models have been applied successfully in biomedical research, but extending them to machine-learning penalties and robust causal estimands is a novel advance.
Where this research is happening
NASHVILLE, UNITED STATES
- VANDERBILT UNIVERSITY MEDICAL CENTER — NASHVILLE, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: SHEPHERD, BRYAN EARL — VANDERBILT UNIVERSITY MEDICAL CENTER
- Study coordinator: SHEPHERD, BRYAN EARL
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.