Generate CONSORT-style equity bias diagrams with standardized mean differences from clinical cohorts.
npx skills add https://github.com/hannesill/m4 --skill equiflow
Please help me install this Agent Skill. Skill: equiflow Source: https://github.com/hannesill/m4/tree/main/src/m4/skills/clinical/equiflow Command: npx skills add https://github.com/hannesill/m4 --skill equiflow
Generates equity-focused cohort flow diagrams that track demographic, socioeconomic, and outcome variables at each exclusion step and compute standardized mean differences to detect selection bias.
Create a CohortFlow from your dataframe, apply exclusions with exclude(), and generate the visualization by calling plot().
High-intent search queries and answers about installing and using this skill.
You can visualize cohort flow and selection bias by applying sequential exclusion steps to a dataframe and rendering a CONSORT-style PDF diagram that overlays standardized mean differences for equity variables at each filtering stage.
The CohortFlow wrapper auto-detects default equity variables like gender, age, and race from your dataset, while also supporting custom variable sets so you can track specific demographic or socioeconomic factors relevant to your research.
You need a pandas dataframe containing your clinical cohort data, along with numpy installed, to use the CohortFlow wrapper for filtering, tracking equity variables, and generating standardized mean difference bias checks.
Standardized mean difference bias checks quantify equity shifts during cohort filtering, measuring how much demographic and socioeconomic distributions deviate at each exclusion step to ensure transparent participant flow reporting.
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