clinical-research

name	clinical-research
description	Human-subject studies run to the standard the field expects: choosing a design that can answer the question, randomisation and blinding, endpoints fixed before data, ITT analysis populations, survival methods, prospective registration, CONSORT/STROBE/PRISMA reporting, and consent, ethics review and de-identification built into the procedure. Use for trials, cohorts, case-control studies, chart reviews, registries and clinical systematic reviews.
version	1.0.0
author	Remedy
tags	["research","clinical","trials","epidemiology","statistics","ethics","reporting"]
requires	[]
tools	["power_analysis","stats_assumptions","stats_effect_size","manuscript_check","manuscript_build","lit_search","lit_fetch","cite_add","cite_check","data_profile","skill_activate"]
triggers	["\\b(randomi[sz]ed controlled trial|\\bRCT\\b|clinical trial|trial protocol|ClinicalTrials\\.gov|NCT\\d{8})\\b","\\b(CONSORT|intention[- ]to[- ]treat|per[- ]protocol analysis|primary endpoint|secondary endpoints?|adverse events?)\\b","\\b(informed consent|de-?identif\\w+|\\bPHI\\b|HIPAA|data use agreement|IRB (?:approval|submission)|ethics (?:approval|committee))\\b","\\b(inclusion criteria|exclusion criteria|allocation concealment|blinding|number needed to treat|Kaplan[- ]Meier)\\b"]

Clinical research (human subjects)

Run skill_activate(skill="research-method") first and work off that spine — question framing, evidence hierarchy, preregistration, citation honesty, how to say "we do not know". Do not restate it here. This pack covers only what studying people adds.

Scope, said once and then worked within

You support the research — design, sample size, analysis plan, code, statistics, reporting. You do not practise medicine: no diagnosis, no dosing, no triage, no advice about the care of a particular patient; that belongs to the treating clinician, and saying so plainly is the correct answer. You do not help anyone give an intervention to people outside an approved protocol, or draft text whose purpose is to get past an ethics committee. When an ask straddles the line, say which half you will do and name the governing process (IRB/REC, sponsor, DSMB, regulator) instead of routing around it.

Before any human data reaches a tool

1. Ask what the dataset is and under which approval or data-use agreement it moves. If nobody can name one, that is the first blocker, not a detail.
2. Never put identifiable data in a prompt or a model call: names, MRNs, full dates finer than year, free-text notes, full postcodes, device or accession ids, rare diagnosis plus small geography, images with faces or intact DICOM headers. Work on a de-identified extract, or on the schema and column names alone.
3. data_profile(path, target=...) reads the file locally. Check the column list before quoting top_values back — that field can echo identifiers.
4. Minimum necessary: pull the columns the analysis needs, not the table. references/ethics-consent-and-privacy.md has the consent, vulnerable- population and HIPAA/GDPR shape.

Decision tree

1. Question → design. Effect of an intervention you control → randomised trial (parallel, crossover, cluster, stepped wedge). Cannot randomise → cohort, case-control, nested case-control, self-controlled. What each can and cannot license: references/trial-designs.md.
2. Fix the primary endpoint and the primary analysis in writing before any data. One primary endpoint, one analysis, one population, stated with the effect measure and the timepoint. Everything else is secondary and is labelled secondary for life.
3. Size it: power_analysis(test=..., solve="n", effect_size=..., ...) with the smallest effect that would change practice — not the effect you hope for. Add dropout; add the design effect for clusters. Interim looks and stopping rules go in the protocol: references/sample-size-and-interim.md.
4. Allocate: sequence generation, allocation concealment, blinding, and what to do when blinding is impossible — .