clinical-research

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: 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. 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. 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 Draft the synopsis — Fill assets/protocol_synopsis_template.md (objectives, design, population, endpoints, statistical plan placeholder, owners-to-sign). 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. 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. Score feasibility — Run phase_gate_scorer.py --input study.json --profile <same> --phase {1|2|3|4} . Read the verdict + blockers + named owners. 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 Script Purpose Profiles scripts/sample_size_estimator.py Power / sample-size for means, proportions, survival n/a (design-driven) scripts/endpoint_selector.py 5-dimension endpoint scoring + classification + surrogate flag drug, device, biologic, diagnostic, digital-therapeutic scripts/phase_gate_scorer.py Feasibility 0-100 + GO/GO-WITH-CONDITIONS/REDESIGN/NO-GO + owners drug, device, biologic, diagnostic, digital-therapeutic All three: stdlib-only, --help , --sample , --output {human,json} . Onboarding & customization Run the onboarding questionnaire once before you start — it captures your defaults and named owners so every tool in this skill is pre-configured. Customization is the point: the answers actually change tool behavior. python3 scripts/onboard.py # interactive (also: --defaults, --set key=value, --reset) python3 scripts/onboard.py --show # see the questions + current effective config Answers are saved to ~/.config/research-ops/clinical-research.json (global) or ./.research-ops/clinical-research.json ( --scope project ) and are read automatically by config_loader.py . They set the default development-area profile , default alpha / power / dropout , and the named biostatistician / medical monitor / regulatory owner printed on outputs. CLI flags always override saved config; RESEARCH_OPS_NO_CONFIG=1 ignores it entirely. The seven questions: development area · alpha · power · dropout · biostatistician · medical monitor · regulatory owner. Optimize with autoresearch (opt-in) This skill ships an isolated, opt-in bridge to engineering/autoresearch-agent . Only when you ask to "optimize" / "run a loop" does an autoresearch experiment iteratively improve a study plan against this skill's own feasibility score. scripts/ar_evaluator.py is the ground-truth evaluator; it prints feasibility_composite: <0-100> (higher is better). /ar:setup --domain custom --name trial-feasibility \ --target study.json \ -- eval "python3 ar_evaluator.py --target study.json" \ --metric feasibility_composite --direction higher /ar:loop custom/trial-feasibility Isolated: no hard dependency — autoresearch runs only on demand, and the loop edits study.json , never the evaluator (locked ground truth). References references/study_design_canon.md — ICH E8(R1) general considerations; ICH E9 + E9(R1) estimand addendum; CONSORT 2010; SPIRIT 2013; FDA Multiple Endpoints guidance (2022). references/endpoint_and_power.md — Cohen Statistical Power Analysis ; Schoenfeld (1983) survival sample size; FDA Surrogate Endpoint Table / BEST glossary; FDA PRO guidance (2009); Chow, Shao & Wang Sample Size Calculations in Clinical Research . references/trial_operations.md — ICH E6(R2/R3) GCP; TransCelerate risk-based monitoring; FDA RBM guidance; CTTI recruitment best practices; site-feasibility scoring literature. Assumptions Sample-size formulas use normal approximations with a built-in z-table. They are first-pass estimates ; a biostatistician produces the final justification (and may use simulation, adaptive designs, or exact methods). The endpoint scorer applies customary regulatory priors per development area via --profile . Company- or indication-specific precedent overrides the prior. The phase-gate scorer bakes in a profile cost-per-patient benchmark; pass a real budget to override the default. An unvalidated surrogate cannot anchor a PRIMARY endpoint — the scorer enforces this with a penalty. Anti-patterns Presenting a power estimate as fact. Every output is an estimate with a named owner who must sign. Powering for a convenience effect size. The effect must trace to a published or anchor-based MCID, not to the n you can afford. Anchoring a primary on an unvalidated surrogate. Surrogate endpoints need validation evidence for the indication. Ignoring multiplicity. More than one primary endpoint requires pre-specified alpha allocation. Skipping dropout inflation. Raw n undersizes the study; inflate by 1/(1 − dropout). Distinct from Sibling / neighbor Scope Difference ra-qm-team ISO 13485 QMS, ISO 14971 risk, EU MDR tech docs + clinical evaluation, FDA 510(k)/PMA/De Novo/QSR submission That is the submission ; clinical-research designs the study beforehand research/grants NIH funding discovery + positioning That finds funding ; this designs the trial product-team/experiment-designer Live product A/B hypothesis + sample size That is a product experiment ; this is a clinical trial research-finance (sibling) R&D program budget + burn That funds the program; this scopes the study Quick examples python3 scripts/sample_size_estimator.py --sample python3 scripts/sample_size_estimator.py --design proportions --p1 0.30 --p2 0.45 --dropout 0.15 python3 scripts/endpoint_selector.py --sample python3 scripts/phase_gate_scorer.py --sample --output json The sample correctly flags an unvalidated serum-cytokine surrogate (cannot be primary) and ranks PASI-75 as the PRIMARY endpoint; the phase-gate sample returns a verdict with a named owner chain. Forcing-question library (Matt Pocock grill discipline) Walked one at a time by /cs:grill-research-ops or the orchestrator. Recommended answer + canon citation per question. Never bundled. "Is your primary endpoint a clinical outcome or a surrogate — and if surrogate, is it on FDA's validated table?" Recommended: clinical outcome unless the surrogate is validated for this indication. Canon: FDA Surrogate Endpoint Table; BEST (Biomarkers, EndpointS, and other Tools) glossary. "What's the minimal clinically important difference you're powering for — and where did that number come from?" Recommended: a published or anchor-based MCID, cited; never a convenience effect size. Canon: ICH E9; Cohen Statistical Power Analysis . "What dropout rate are you assuming, and is the sample size inflated for it?" Recommended: inflate n by 1/(1 − dropout) using a justified rate. Canon: Chow, Shao & Wang; ICH E9(R1). "Single primary endpoint or multiple — and if multiple, what's the multiplicity control?" Recommended: pre-specify alpha allocation (hierarchical / Bonferroni). Canon: FDA Multiple Endpoints guidance (2022). "Who is the named biostatistician / medical monitor / regulatory owner signing this synopsis?" Recommended: name them now — this output is a recommendation, not a protocol. Canon: ICH E6(R2) GCP roles & responsibilities. Walk depth-first. Lock 1-2 before opening 3-5. After all are answered, invoke endpoint_selector.py → sample_size_estimator.py → phase_gate_scorer.py .