clinical-research

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
name: clinical-research
description: Use when designing a prospective clinical study before submission — selecting and classifying endpoints (primary / key-secondary / exploratory, with surrogate-endpoint flagging), estimating sample size and power for two-arm designs (means / proportions / survival), or scoring a study plan for feasibility and a GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO phase-gate decision. Every output is an ESTIMATE plus a named human owner (clinician / biostatistician / regulatory owner) — never clinical fact, never a finished protocol. Distinct from ra-qm-team, which handles the regulatory/QM submission (ISO 13485, EU MDR, FDA 510(k)/PMA/QSR), not the study design.
version: 2.9.0
author: claude-code-skills
license: MIT
tags: [research-ops, clinical-research, study-design, endpoint, sample-size, power, phase-gate, biostatistics]
compatible_tools: [claude-code, codex-cli, cursor, antigravity, opencode, gemini-cli]
---

# clinical-research

Prospective clinical study DESIGN: endpoints, sample size / power, and phase-gate feasibility. Every output is an **estimate with stated assumptions** routed to a **named human owner**. This skill never gives clinical advice as fact and never substitutes for a biostatistician or regulatory affairs.

## Purpose

R&D clinical teams, medical monitors, and biostatistics functions live at the moment between *we-have-a-hypothesis* and *we-have-a-protocol-ready-for-submission*. This skill structures three of the hardest design decisions:

Three deterministic tools:

1. `sample_size_estimator.py` — Closed-form power / sample-size for two-arm **means** (Cohen's d), **proportions** (normal approximation), and **survival** (Schoenfeld events). Inflates for dropout. Prints an "ESTIMATE — confirm with a biostatistician" banner.
2. `endpoint_selector.py` — Scores candidate endpoints across 5 weighted dimensions (clinical relevance, measurability, regulatory acceptance, sensitivity-to-change, burden) and classifies each as **PRIMARY / KEY-SECONDARY / EXPLORATORY**. Penalizes unvalidated surrogate endpoints.
3. `phase_gate_scorer.py` — Scores a study plan 0-100 across recruitment feasibility, endpoint readiness, statistical power, operational complexity, and budget fit; returns **GO / GO-WITH-CONDITIONS / REDESIGN / NO-GO** plus the named owners who must sign.

## When to use

Invoke this skill when:

- You are choosing a primary endpoint and need to defend it against surrogate-endpoint scrutiny.
- You need a defensible first sample-size estimate for a protocol synopsis.
- A study plan needs a feasibility read before a phase-gate review.
- You are pressure-testing whether the planned enrollment is achievable given the eligible population and sites.

**Do NOT use this skill to**: prepare a regulatory submission or clinical evaluation report (use `ra-qm-team`), find or position a grant (use `research/grants`), design a live product A/B experiment (use `product-team/experiment-designer`), or replace a biostatistician's final sample-size justification.

## Workflow

1. **Draft the synopsis** — Fill `assets/protocol_synopsis_template.md` (objectives, design, population, endpoints, statistical plan placeholder, owners-to-sign).
2. **Select the endpoint** — Run `endpoint_selector.py --input endpoints.json --profile {drug|device|biologic|diagnostic|digital-therapeutic}`. Read the classification + surrogate flags. If >1 primary, plan multiplicity control.
3. **Estimate the sample size** — Run `sample_size_estimator.py --design {means|proportions|survival} ...`. Trace the effect/difference/HR to a published or anchor-based source; inflate for dropout.
4. **Score feasibility** — Run `phase_gate_scorer.py --input study.json --profile <same> --phase {1|2|3|4}`. Read the verdict + blockers + named owners.
5. **Route for sign-off** — Assemble the synopsis + estimates into the gate packet. The packet is **a recommendation**; a biostatistician, medical monitor, and regulatory owner sign.

## Scripts