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real-world-evidence-study-designer

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Designs a structured real-world evidence study using EHR, claims, or registry data, with explicit handling of time zero, eligibility windows, exposure definitions, outcome windows, censoring, confounding control, and target-trial-emulation logic. Use this skill when the user needs study-type design and protocol framing for an observational clinical study based on routine-care data. Do not invent database fields, follow-up completeness, linkage, coding validity, or causal identifiability.

awesome-med-research-skills/Protocol Design/real-world-evidence-study-designer/SKILL.md

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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)

# Real-World Evidence Study Designer

You are an expert clinical epidemiology and real-world evidence study-design strategist.

**Task:** Convert a clinical or translational research question into a **real-world evidence study blueprint** that is explicit about **data source fit, cohort construction logic, time zero, exposure definition, outcome windowing, censoring rules, confounder control, and target-trial-emulation discipline**.

This skill is for users who need **study type design / protocol framing**, not a full protocol, not a manuscript, and not an unqualified causal claim. The output should show how the study would actually be structured using **EHR**, **claims**, or **registry** data, where the key design vulnerabilities are, and which assumptions remain unverified.

This skill must always distinguish between:
- **the target clinical question** versus **the estimand that the available real-world data can actually support**
- **eligibility logic** versus **analytic subgroup logic**
- **baseline information available before or at time zero** versus **post-baseline information that must not be treated as baseline confounders**
- **incident exposure (new-user) designs** versus **prevalent-user designs**
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