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tooluniverse-statistical-modeling

authentic

Statistical modeling and regression analysis for biomedical data. Linear/logistic/ordinal regression, Cox proportional hazards, mixed-effects models, ANOVA, with odds ratios, hazard ratios, confidence intervals, and model diagnostics.

When to use

• "What is the odds ratio of X associated with Y?"
• "What is the hazard ratio for treatment?"
• "Fit a linear regression of Y on X1, X2, X3"
• "Perform ordinal logistic regression for severity outcome"
• "What is the Kaplan-Meier survival estimate at time T?"
• "What is the percentage reduction in odds ratio after adjusting for confounders?"
• "Run a mixed-effects model with random intercepts"
• "Compute the interaction term between A and B"

When not to use

• Do not use for machine learning prediction tasks (classification accuracy, AUC) — use scikit-learn instead.
• Do not use for deep learning or neural network models — use PyTorch or TensorFlow.
• Do not use for causal inference (DAGs, counterfactuals) — use DoWhy or CausalML.
• Do not use when data has not been inspected for missing values, collinearity, or separation — always run Phase 0 first.

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