stat-modeling-tools

Files (3)

• SKILL.md
• stat_test_report.py · templates/
• statsmodels_regression.py · templates/

SKILL.md (first lines)

# Stat Modeling Tools

Use this skill when the user needs reproducible statistical analysis rather than only visual inspection.

Typical triggers:
- choose or run a hypothesis test on tabular data
- compare two groups or test association between variables
- fit OLS, logistic, or Poisson models with coefficient tables
- inspect residuals, p-values, confidence intervals, or effect sizes
- generate machine-readable statistical summaries for a manuscript or report

## Environment Check

```bash
which python3 || true
python3 - <<'PY'
mods = ["numpy", "pandas", "scipy", "statsmodels"]
for name in mods:
    try:
        __import__(name)
        print(f"{name}: ok")
    except Exception as exc:
        print(f"{name}: missing ({exc})")
PY
```

If key modules are missing, say so explicitly and recommend the optional `drug-sandbox` image documented in `docs/operations/science-runtime.md`.

## Bundled Assets

- `templates/stat_test_report.py`
- `templates/statsmodels_regression.py`

## Preferred Workflow

1. Identify outcome type first: continuous, binary, count, or categorical contingency table.
2. Run a small deterministic statistical summary before fitting a larger model.
3. Report effect sizes and confidence intervals, not only p-values.
4. Save CSV and JSON outputs so the result is reusable.
5. Keep claim scope tied to the study design. Statistical association is not causal proof.

## Hypothesis Tests

```bash
python3 templates/stat_test_report.py \
  --input stats/assay.csv \
  --test independent_ttest \
  --value-column response \
  --group-column arm \
  --group-a control \
  --group-b treated \
  --output stats/assay_ttest.csv \
  --summary stats/assay_ttest.json
```

Supported baseline tests in the bundled template:
- `independent_ttest`
- `paired_ttest`
- `mannwhitney`
- `chi_square`
- `pearson`
- `spearman`

Use this for quick but explicit statistical reporting.

## Regression With Statsmodels

```bash
python3 templates/statsmodels_regression.py \
  --input stats/cohort.csv \
  --model ols \
  --outcome response \
  --feature age \
  --feature dose \
  --feature biomarker \
  --output stats/ols_coefficients.csv \
  --summary stats/ols_summary.json
```

Supported baseline models in the bundled template:
- `ols`
- `logit`
- `poisson`

Use this for:
- coefficient tables with confidence intervals
- basic inference and model-fit summaries
- prediction export for downstream review

## Working Rules

- Prefer exact test names and explicit group labels.
- Check whether the data are paired before running paired tests.
- For regression, list the exact feature set and reference coding assumptions.
- Do not oversell significance when effect sizes are trivial.
- Distinguish exploratory testing from pre-specified confirmatory analysis.

## Related Skills

For Kaplan-Meier, Cox models, and time-to-event workflows, activate `survival-analysis-tools`.
For static or interactive figures, activate `scientific-visualization-tools`.
For study design, reproducibility planning, or manuscript critique, activate `scientific-workflow-tools` or `clinical-research-tools`.

From DrugClaw/DrugClaw, Apache-2.0.

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