FRESH: Information-Geometric Calibration of Patient-Level Models to Aggregate Evidence

TL;DR

FRESH is a novel method that integrates aggregate population-level data with patient-level models using information geometry, enhancing clinical decision-making and trial analysis.

Contribution

It introduces a principled, information-geometric approach to recalibrate patient-level models with aggregate data, enabling more accurate and data-efficient clinical predictions.

Findings

Enables incorporation of summary statistics into patient-level models.

Provides a minimal perturbation recalibration in an information-geometric sense.

Facilitates applications like trial contextualization and comparative-effectiveness analysis.

Abstract

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.