A new integrative learning framework for integrating multiple secondary outcomes into primary outcome analysis: a case study on liver health
Daxuan Deng, Peisong Han, Shuo Chen, Ming Wang, Chixiang Chen · 2025
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Description
Clinical studies collect many side measures, from blood chemistry to urine tests, that usually sit unused while researchers judge the main outcome. Daxuan Deng and co-authors propose a learning framework that folds those secondary outcomes into the main analysis. Older methods demand strong model assumptions or prior knowledge to combine the extra data. Their approach instead learns the links from the data itself and stays robust when parts of the model are wrong.
In simulations, the method cut the variance of the main estimate and beat rival approaches. Applied to UK Biobank records, it linked cigarette smoking with greater liver fat, showing how spare measurements can sharpen a health finding.
Catalog record
| CREATOR | Daxuan Deng, Peisong Han, Shuo Chen, Ming Wang, Chixiang Chen |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 66 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |