FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866916016378871808 |
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| author | Fuller, Franklin Bertolini, Daniele Liang, Samantha Christopher, Jason Smith, Aaron M. |
| author_facet | Fuller, Franklin Bertolini, Daniele Liang, Samantha Christopher, Jason Smith, Aaron M. |
| contents | This note introduces FRESH (Fusion of Recent Evidence and Subject Histories), a method for incorporating population-level summary results -- published clinical trials, registry summaries,
prior natural-history studies, and peer-reviewed indirect comparisons -- into predictive models trained on patient-level data. This method provides a principled means of combining both
patient-level and aggregate-level data types into a unified data-efficient model for clinical decision making.
FRESH assumes access to a generative model trained on patient-level data sources (e.g. clinical trial or real-world data). The method produces patient-level predictions from a re-calibrated
model that matches a set of specified aggregate statistics for a target population. This can be understood as a patient-level recapitulation of the aggregate source -- with the key property
that the recalibration is a minimal perturbation of the original joint distribution in a specific information-geometric sense. The resulting samples can be analyzed directly or combined into a
post-training procedure to update the original generative model.
This approach enables several applications where rigorously incorporating patient-level data with summary information is valuable, including (i) contextualizing single-arm trial results with
respect to recent standard-of-care, (ii) clinical-trial simulations for design and probability-of-technical-success estimation, and (iii) comparative-effectiveness analyses of on-market
therapies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16246 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence Fuller, Franklin Bertolini, Daniele Liang, Samantha Christopher, Jason Smith, Aaron M. Methodology Machine Learning This note introduces FRESH (Fusion of Recent Evidence and Subject Histories), a method for incorporating population-level summary results -- published clinical trials, registry summaries, prior natural-history studies, and peer-reviewed indirect comparisons -- into predictive models trained on patient-level data. This method provides a principled means of combining both patient-level and aggregate-level data types into a unified data-efficient model for clinical decision making. FRESH assumes access to a generative model trained on patient-level data sources (e.g. clinical trial or real-world data). The method produces patient-level predictions from a re-calibrated model that matches a set of specified aggregate statistics for a target population. This can be understood as a patient-level recapitulation of the aggregate source -- with the key property that the recalibration is a minimal perturbation of the original joint distribution in a specific information-geometric sense. The resulting samples can be analyzed directly or combined into a post-training procedure to update the original generative model. This approach enables several applications where rigorously incorporating patient-level data with summary information is valuable, including (i) contextualizing single-arm trial results with respect to recent standard-of-care, (ii) clinical-trial simulations for design and probability-of-technical-success estimation, and (iii) comparative-effectiveness analyses of on-market therapies. |
| title | FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2605.16246 |