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 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-\3, 000) /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.