# RWE-Agent

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Nayan Chaudhary](https://cerebralvalley.ai/u/nayanchaudhary)
- **GitHub:** https://github.com/nayanchaudhary/claude-science-rweAgent
- **Demo video:** https://youtu.be/l-CmxNZDHK4
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/289

What we built. We created a Real-World Evidence Methodologist — an AI specialist that acts as a built-in epidemiologist and causal-inference expert, sitting alongside any life-sciences analysis that relies on observational data (patient registries, electronic health records, insurance claims, biobanks, or clinical-trial data being reused for a new question). 

Why it matters. In life sciences, the hard part of learning from real-world data is almost never running the statistics — it's the design decisions made before the analysis: who counts as a patient, how a disease or drug exposure is actually defined in messy data, and when the clock starts for each person. Get those wrong and you get confident, published-looking answers that are simply artifacts of bias — a drug looks protective when it isn't, a biomarker looks predictive when it's really just tracking how sick someone was to begin with. This specialist exists to stop that failure at the source. 

What it does. Instead of jumping straight to a result, it first asks how comfortable you are with these methods and adjusts its language to match — full technical depth for a biostatistician, plain guidance for a bench scientist or clinician — then walks you through the study design interactively, turning each choice into a simple picture you approve: a cohort diagram showing exactly who's included or excluded and why, side-by-side cards comparing candidate definitions of a condition or outcome, and timelines that visually expose traps like "immortal time" and selection bias. Only once the design is sound does it move to any computation, and it flags every assumption and hidden pitfall along the way. 

Whom it impacts: It lowers the barrier for the many researchers, clinicians, and translational scientists who have valuable real-world datasets but aren't trained epidemiologists — letting them ask rigorous causal questions without accidentally fooling themselves — while giving trained methodologists a faster, visual, reproducible way to specify and document a defensible study. The net effect is fewer spurious findings, more trustworthy evidence, and study designs that are transparent and reviewable before a single result is generated.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/289. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt