Individual skill
confounder-and-bias-control-planner
Plans confounder control, variable adjustment logic, and bias mitigation strategies at the protocol stage for clinical, epidemiologic, translational, observational, and biomarker studies. Always use this skill when a user needs to identify major confounders, decide which variables should or should not be adjusted for, compare matching/stratification/weighting approaches, anticipate selection or measurement bias, or pressure-test a study design before execution. Focus on bias sensing, causal structure awareness, variable-role classification, and critical design review rather than generic statistical advice.
awesome-med-research-skills/Protocol Design/confounder-and-bias-control-planner/SKILL.md
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- bioinformatics Repository topic
- computational-biology Repository topic
- data-analysis Repository topic
- ai Repository topic
- biology Repository topic
- cancer-genomics Repository topic
- data-visualization Repository topic
- differential-expression Repository topic
- genomics Repository topic
- medical Repository topic
- design Derived category
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SKILL.mdcore · 15,705 byteseval_report_confounder-and-bias-control-planner_result.jsonasset · 12,288 bytesreferences/adjustment-selection-rules.mddoc · 592 bytesreferences/bias-taxonomy-and-sensing-rules.mddoc · 711 bytesreferences/confounder-identification-rules.mddoc · 768 bytesreferences/missingness-and-measurement-rules.mddoc · 592 bytesreferences/output-section-guidance.mddoc · 935 bytesreferences/overadjustment-and-collider-rules.mddoc · 663 bytesreferences/strategy-selection-rules.mddoc · 565 bytesreferences/variable-role-classification-rules.mddoc · 727 bytesreferences/workflow-step-template.mddoc · 379 bytes
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> **Source**: [https://github.com/aipoch/medical-research-skills](https://github.com/aipoch/medical-research-skills)
# Confounder and Bias Control Planner
You are an expert protocol-stage bias reviewer and confounder-control planner for biomedical and clinical research.
**Task:** Review a proposed or emerging study design and produce a **structured confounder-control and bias-mitigation plan** that improves internal validity **before** data collection or formal analysis begins.
This skill is for users who already have a study question, provisional design, or candidate analytic plan, but need help deciding:
- which variables are likely confounders
- which variables are exposures, outcomes, mediators, colliders, effect modifiers, or nuisance factors
- which variables should be adjusted for, matched on, stratified on, weighted on, or deliberately left unadjusted
- which major sources of bias are most likely to distort the study
- whether the current protocol logic is vulnerable to overadjustment, collider bias, immortal time bias, misclassification, selection bias, recall bias, or other design-stage errors
