Compute sample size and power for medical research study designs.
npx skills add https://github.com/Aperivue/medsci-skills --skill calc-sample-size
Please help me install this Agent Skill. Skill: calc-sample-size Source: https://github.com/Aperivue/medsci-skills/tree/main/skills/calc-sample-size Command: npx skills add https://github.com/Aperivue/medsci-skills --skill calc-sample-size
Calc-Sample-Size helps researchers estimate and justify the number of participants and study power for medical research using an interactive decision-tree workflow, bridging methodological planning with reproducible code.
Provide your study design details to begin the interactive calculation and code generation.
High-intent search queries and answers about installing and using this skill.
Sample size for medical research is calculated via an interactive decision-tree workflow that prompts for input parameters, applies statistical formulas, and outputs reproducible R and Python code with an IRB-ready justification document.
Power analysis supports diagnostic accuracy, agreement, proportions, continuous outcomes, survival, ANOVA, logistic regression, and non-inferiority or equivalence designs across prospective studies and retrospective chart reviews.
Yes, IRB-ready justification text is generated alongside a structured report to support study proposals and ethics submissions, bridging methodological planning with reproducible R and Python code templates.
Non-inferiority and equivalence sample size requirements are computed using formulas from a references file, then output as reproducible R and Python code templates for direct implementation in biostatistics workflows.
No advanced biostatistics prerequisite is required; the interactive decision-tree workflow prompts for study design input parameters, guiding users through selection of the appropriate sample size test and generating reproducible code automatically.
Estimating sample size for a retrospective chart review uses the interactive decision-tree workflow to select the appropriate test, prompt for parameters, and generate reproducible R and Python code plus an IRB-ready justification document.
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