/cs-clinical-research

/cs:clinical-research — Endpoint selection + sample-size + phase-gate feasibility

Run the clinical-research skill on this input:

$ARGUMENTS

Three-tool workflow

1. endpoint_selector.py — Score candidate endpoints across clinical relevance, measurability, regulatory acceptance, sensitivity-to-change, and burden. Classify PRIMARY / KEY-SECONDARY / EXPLORATORY. Flags unvalidated surrogates (cannot be primary). Industry tuning via --profile.

2. sample_size_estimator.py — Closed-form power / sample size for two-arm means (Cohen's d), proportions (normal approx), or survival (Schoenfeld events). Inflates for dropout. The effect/difference/HR must trace to a published or anchor-based source.

3. phase_gate_scorer.py — Score the study plan 0-100 across recruitment feasibility, endpoint readiness, statistical power, operational complexity, and budget fit. Verdict + named owners (PI, Medical Monitor, Biostatistician, Regulatory Owner).

Output

• Endpoint classification + surrogate flags
• Sample-size estimate with assumptions block
• Phase-gate verdict with named owner chain
• Top 3 next actions

Hard rule

Every output is an ESTIMATE, not a protocol. A biostatistician, medical monitor, and regulatory owner sign the final design.

First run + optimization

• Onboard first: python3 skills/clinical-research/scripts/onboard.py (area, alpha, power, dropout, named owners) — saved config pre-configures every tool. --show lists the questions.
• Optimize (opt-in): only if the user asks to optimize/run a loop, hand off to autoresearch via skills/clinical-research/scripts/ar_evaluator.py (feasibility_composite, higher is better).

Distinct from

• ra-qm-team — that's the regulatory submission. This designs the study.
• research/grants — that finds funding. This designs the trial.
• product-team/experiment-designer — that's a product A/B. This is a clinical trial.