The Continuous Evidence Generation Protocol: Two-Stage Validation (RWE → Pragmatic Trials)

Website: https://manual.warondisease.org/knowledge/appendix/dfda-spec-paper.html

Abstract: Treatments that could save lives take an average of 8.2 years (95% CI: 4.85 years-11.5 years) to complete clinical trials after discovery. Since 1962, these delays have contributed to an estimated 102 million deaths (95% CI: 36.9 million deaths-214 million deaths) preventable deaths. Meanwhile, only 1-10% of adverse drug events get reported to the FDA, and billions of people generate continuous health data through wearables and apps that remains unharvested. We present a two-stage framework that transforms this data into validated treatment recommendations. Stage 1 (\$0.1 (95% CI: \$0.03-\$1)/patient): aggregate millions of natural experiments and score causal confidence using the Predictor Impact Score (PIS), a composite metric operationalizing six Bradford Hill causality criteria. Stage 2 (\$929 (95% CI: \$97-\$3K)/patient): confirm top signals through pragmatic trials embedded in routine care, 44.1x (95% CI: 39.4x-89.1x) cheaper than traditional Phase III trials. Cost estimates derive from a meta-analysis of 108 pragmatic trials plus implementations like RECOVERY (which found a life-saving treatment in 100 days) and ADAPTABLE. A Trial Priority Score (PIS x DALYs x Novelty x Feasibility) determines which signals proceed to experimental confirmation. The framework produces three outputs absent from current pharmacovigilance: (1) "Outcome Labels," per-condition documents ranking all treatments by quantitative effect size (inverting the traditional per-drug FDA label paradigm); (2) precision dosing recommendations derived from optimal daily values (the predictor values historically preceding the best outcomes); and (3) a three-tier evidence grading system (Validated, Promising, Signal) combining observational and experimental effect sizes. Trial results feed back to calibrate observational models, creating a learning health system where accuracy improves continuously. High PIS signals warrant experimental investigation; low PIS does not rule out true effects. This framework complements traditional RCTs. Stage 2 pragmatic trials are required to establish validated causal claims.

Summary: We present the Predictor Impact Score (PIS), a novel composite metric operationalizing Bradford Hill causality criteria for automated signal detection from aggregated N-of-1 observational studies. Combined with pragmatic trial confirmation (based on evidence from 108+ embedded trials), this two-stage framework would generate validated outcome labels at 44.1x lower cost than traditional Phase III trials. This enables continuous, population-scale pharmacovigilance and precision dosing recommendations.