bio-clinical-biostatistics-logistic-regression
Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.
npx skills add BioTender-max/awesome-bio-agent-skills --skill logistic-regression --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: statsmodels 0.14+, scipy 1.12+, numpy 1.26+, pandas 2.1+, firthmodels 0.3+, marginaleffects 0.0.13+ (Python) / 0.20+ (R). R packages cited: RobinCar, marginaleffects, brant, MASS, VGAM. Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Logistic Regression for Clinical Outcomes **"Model clinical outcomes with logistic regression"** -> Estimate the marginal or conditional treatment effect on a binary or ordinal endpoint using a model that respects randomisation stratification, declares its estimand, and survives covariate misspecification. **Conditional vs marginal (the non-collapsibility subtlety):** the OR is non-collapsible. The conditional OR from logistic regression is a *different parameter* than the marginal OR, even when there is NO confounding and randomisation is perfect. This is mathematical, not s
- Version Compatibility
- Algorithmic Taxonomy
- Decision Tree by Scenario
- Standard Workflow
- Marginal vs Conditional Estimand -- The FDA 2023 Pivot
- G-computation / Standardisation
- Modified Poisson for Common Outcomes -- Direct RR
- Proportional Odds and Brant Test
- Separation and Firth Penalty
- Hauck-Donner Effect Detection
- Reconciliation: When Methods Disagree
- Per-Method Failure Modes
- Reference-category silent reversal
- Adjusting for a mediator
What does the bio-clinical-biostatistics-logistic-regression skill do?
Performs logistic regression for clinical trial outcomes (binary, ordinal, multinomial) with marginal-vs-conditional estimand reporting per FDA 2023 covariate adjustment guidance, g-computation/standardisation for marginal effects, modified Poisson for RR, Brant test for proportional odds, Firth penalty for separation, and Hauck-Donner detection. Use when modeling binary or ordinal endpoints in confirmatory or exploratory clinical trials.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill logistic-regression --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.