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tooluniverse-statistical-modeling

Fit biomedical regression models and compute effect estimates with statsmodels and lifelines.

Updated Apr 18, 2026
Search Tags:#survival-analysis#statistical-modeling#mixed-effects#logistic-regression#hazard-ratio#model-diagnostics#odds-ratio
One-click install
npx skills add https://github.com/Centaurioun/osteogenesis_imperfecta --skill tooluniverse-statistical-modeling
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: tooluniverse-statistical-modeling
Source: https://github.com/Centaurioun/osteogenesis_imperfecta/tree/main/.agents/skills/tooluniverse-statistical-modeling
Command: npx skills add https://github.com/Centaurioun/osteogenesis_imperfecta --skill tooluniverse-statistical-modeling

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, scipy, statsmodels, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Enable reproducible, publication-ready statistical modeling for biomedical and clinical datasets by providing clear workflows to select, fit, and interpret linear, logistic, ordinal, mixed-effects, and survival models. Reduce errors and save time by standardizing model selection, diagnostics, and formatted reporting so analysts can focus on interpretation and decision-making.

Core Features & Use Cases

  • Model selection & fitting: linear regression, binary/ordinal/multinomial logistic, mixed-effects models, Cox proportional hazards, Kaplan-Meier.
  • Effect estimation: compute odds ratios, hazard ratios, confidence intervals, p-values, and percentage change comparisons between crude and adjusted models.
  • Model diagnostics & formatting: automated diagnostic checks (VIF, Breusch-Pagan, Shapiro-Wilk, proportional hazards checks), per-feature ANOVA pipelines for genomic data, and publication-ready summary tables.
  • Real-world use case: fit an ordinal logistic model for COVID-19 severity, extract the OR for vaccination with 95% CI and p-value, run proportional-odds checks, and produce a formatted table for manuscript inclusion.

Quick Start

Fit the appropriate regression (e.g., logistic, ordinal, Cox, mixed-effects) on my dataset and return effect sizes (OR/HR), 95% confidence intervals, p-values, diagnostics, and a publication-ready summary.

Frequently Asked Questions about tooluniverse-statistical-modeling

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute odds ratios and hazard ratios with confidence intervals in Python?▼

Compute odds ratios and hazard ratios with confidence intervals by fitting logistic, Cox, or mixed-effects models on pandas dataframes using statsmodels and lifelines, returning effect estimates and inferential statistics for biomedical datasets.

How do I run model diagnostics for logistic regression and survival analysis?▼

Run model diagnostics for logistic regression and survival analysis by applying automated checks like VIF, Breusch-Pagan, Shapiro-Wilk, and proportional hazards tests to validate statistical modeling assumptions before generating publication-ready summary tables.

Can I use pandas dataframes with statsmodels for biomedical regression analysis?▼

Use pandas dataframes with statsmodels to perform biomedical regression analysis across linear, binary, ordinal, multinomial logistic, and mixed-effects models, generating formatted effect estimates and diagnostic test outputs compatible with clinical workflows.

What's the best way to format publication-ready summary tables for Cox proportional hazards models?▼

Format publication-ready summary tables for Cox proportional hazards models by fitting survival analyses and extracting hazard ratios, confidence intervals, p-values, and proportional hazards checks into standardized outputs for manuscript inclusion.

Does this statistical modeling approach support per-feature ANOVA pipelines for genomic data?▼

This statistical modeling approach supports per-feature ANOVA pipelines for genomic data by applying group comparisons and inferential statistics across experimental workflows, integrating seamlessly with pandas dataframes and scipy-driven analyses.

When should I use mixed-effects models instead of standard logistic regression for clinical datasets?▼

Use mixed-effects models instead of standard logistic regression for clinical datasets when handling hierarchical or repeated measures data, computing odds ratios and model diagnostics while accounting for random effects across grouped experimental workflows.

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