Clinic Sample Size

Source: https://github.com/aipoch/medical-research-skills

When to Use

• Use this skill when the request matches its documented task boundary.
• Use it when the user can provide the required inputs and expects a structured deliverable.
• Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.

Key Features

• Smart Inference: If specific statistical parameters (e.g., standard deviation, error margins) are not provided, the tool infers reasonable defaults based on the study type and mode (e.g., assumes medium effect size for t-tests, standard alpha/beta levels).
• Metadata Support: Allows attaching study names and outcome measures to the final report.
• Multilingual Reports: Generates detailed Markdown reports in Chinese.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

See ## Usage above for related details.

cd "20260316/scientific-skills/Others/clinic-sample-size"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/calculators.py with additional helper scripts under scripts/.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Validation Shortcut

Run this minimal command first to verify the supported execution path:

python scripts/validate_skill.py --help

Clinic Sample Size Calculator

This skill integrates sample size calculations for four major types of clinical research: Diagnostic, Efficacy, Etiology, and Prognosis.

Output

The calculation results are saved as Markdown files in the output/ directory by default. The file path is returned in the JSON output. You can specify a custom output directory using --output_dir.

Usage

The skill is executed via the scripts/main.py script. The first argument specifies the study type: diagnostic, efficacy, etiology, or prognosis.

Optional Metadata

You can add study information to the report using:

• --study_name <name>: Name of the study.
• --outcome <name>: Primary outcome measure.

1. Diagnostic Studies

Calculates sample size for sensitivity/specificity, AUC, Kappa, or multivariable models.

Example: Sensitivity/Specificity (Full Params)

python scripts/main.py --study_name "New Biomarker Study" --outcome "Sensitivity > 0.8" diagnostic sens_spec --se 0.8 --sp 0.9 --error 0.05 --prev 0.3 --dropout 0.1

Example: Sensitivity/Specificity (Smart Inference)

# Only providing study name/outcome, tool infers se=0.85, sp=0.90, prev=0.5, etc.
python scripts/main.py --study_name "Screening Test" --outcome "Diagnosis" diagnostic sens_spec

2. Efficacy Studies

Calculates sample size for randomized controlled trials (RCTs) or single-arm studies. Input: JSON string or file.

Example: Two-arm General (Smart Inference)

# Infers MeanT=0.5, MeanC=0 (Medium effect size), St=1, Sc=1
python scripts/main.py --study_name "Drug Trial" --outcome "Pain Score" efficacy --input '{"study_type": "general", "design": "two"}'

3. Etiology Studies

Calculates sample size for cohort/case-control studies.

Example: Categorical (Smart Inference)

# Infers Pt=0.2, Pc=0.1 (RR=2.0)
python scripts/main.py --study_name "Risk Factor Study" etiology --mode categorical

4. Prognosis Studies

Calculates sample size for prediction models or prognostic factors.

Example: Prediction Model (EPV)

# Infers P=0.1 (Event rate), training_rate=0.7
python scripts/main.py prognosis epv --variables_number 10

When Not to Use

• Do not use this skill when the required source data, identifiers, files, or credentials are missing.
• Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
• Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.