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.

What does this entry provide?

bio-clinical-biostatistics-logistic-regression is an agent skill entry in ChatHome Skills, sourced from GitHub Skill Search and scored 58/100. ## 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+ (Py. As an agent skill, it packages a reusable instruction set that AI coding agents such as Codex and Claude Code can load to perform this task without hand-written glue code. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding bio-clinical-biostatistics-logistic-regression to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.

Snapshot facts

Source: GitHub Skill Search
Quality: 58/100
Install method: codex
Repo: huang-sh/DeepScience
Repo updated: Oct 28, 2026

Install command

codex skill install huang-sh/DeepScience

Install method: codex

Review the upstream source before running this command.

GitHub project

Quality

Repo stars

Repo updated

Unknown

How is this score calculated?

Weighted across six dimensions, scored 0-100.
Provenance (25): listed in an official or curated registry.
Installability (25): a runnable install command beats a declared transport, which beats a source checkout.
Maintenance (20): recency of the last push; archived entries score zero here.
Adoption (20): repository stars on a log curve.

Security signals

Scan coverage: Install metadata and the indexed document body.

Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.

Discovery note

## 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

Report an issue with this listing