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# 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 | 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. ```bash 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). ```bash /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 ```bash 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. 1. **"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. 2. **"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*. 3. **"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). 4. **"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). 5. **"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`.