/cs-clinical-research

description: Clinical study design. Select and classify endpoints, estimate sample size / power (means / proportions / survival), and score a study plan for a GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO phase-gate decision. Every output is an ESTIMATE plus a named clinical owner — never clinical fact. Direct invocation of the clinical-research skill.
argument-hint: "<study context: indication, design, endpoints, effect size, target enrollment>"

/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**.