frekil.comCrawled Oct 16, 2026
The page describes tracking competitor evidence activity, treatment-switching patterns, and formulary shifts using real-world data before they reach a pipeline.
Generate cost-effectiveness, budget impact, and comparative effectiveness evidence in days, not months. Hit formulary windows with audit-ready HEOR studies.
The page outlines four steps from raw clinical data to publication-ready evidence: Structure, Design, Analyze, and Report. Patient data never leaves the user's environment.
The page describes monitoring therapy performance across real-world populations, with outcomes, utilization, and evidence gaps refreshed on demand.
The page features essays on real-world evidence strategy, RWE methodology, and lessons from building an evidence infrastructure layer for life sciences.
A statistically significant result in observational data often is not. Here is the actual math of negative controls and empirical calibration - the empirical null model, the calibrated p-value formula, worked examples, and the assumption it all rests on.
A practical walkthrough of causal inference for RWE: why naive comparisons fail, how DAGs clarify assumptions, and how target trial emulation turns messy data into a credible study design.
A plain-English technical guide to propensity scores for survival RWE, using a post-MI statin example to explain matching, weighting, estimands, survival curves, balance checks, and sensitivity analysis.
Frekil provides real-world evidence infrastructure for life sciences, converting raw clinical data into regulatory-grade evidence in minutes. It is YC-backed.
The page describes self-improving AI agents that test hypotheses, build cohorts, and run end-to-end studies on the user's clinical data, generating real-world evidence in minutes rather than months.
Frekil's privacy policy describes how it handles data, what it collects, and users' rights.
Frekil's terms of service govern use of the Frekil platform.
