Skill: Clinical Trial Statistical Analysis Planning
Plan rigorous statistical analyses for clinical trial protocols
What You Can Do
This skill guides you through designing comprehensive statistical analysis plans (SAPs) for clinical trials, ensuring regulatory compliance and methodological rigor. You'll define primary and secondary endpoints, determine appropriate sample sizes, specify statistical tests, and document pre-specified analyses that minimize bias and maximize scientific validity. The skill helps you translate clinical hypotheses into precise statistical frameworks that withstand peer review and regulatory scrutiny.
Features
Systematically define primary, secondary, and exploratory endpoints with clear efficacy criteria, timing windows, and outcome measures aligned to trial objectives.
Calculate required sample sizes based on effect sizes, statistical power, significance levels, and assumptions, with sensitivity analyses for different scenarios.
Recommend appropriate statistical tests for your trial design (parallel, crossover, adaptive) accounting for data distributions, missing data patterns, and multiplicity.
Specify handling approaches for dropout and missing values including sensitivity analyses and imputation methods aligned with regulatory guidance (FDA, EMA).
Design multiple comparison corrections and interim analysis plans that maintain family-wise error rates while protecting trial integrity.
Pre-specify subgroup analyses, interaction tests, and handling criteria to prevent spurious findings while exploring clinically relevant heterogeneity.
Define safety endpoints, adverse event categorization, clinical significance thresholds, and statistical monitoring rules for patient protection.
Generate structured analysis plan sections ready for adaptation into your protocol supplement with rationales, assumptions, and contingency plans.
Example Output
Sample Size Calculation:
- Primary endpoint: Time to disease progression
- Hazard ratio: 0.67 (33% risk reduction)
- Power: 90%, α: 0.05 (two-sided)
- Expected median survival: Control 12 months, Treatment 18 months
- Required events: 187 progressions
- Estimated sample size: 240 patients (accounting for 10% early withdrawal)
Primary Analysis Set:
- Population: Intent-to-treat (ITT) — all randomized patients
- Test: Kaplan-Meier curves with log-rank test stratified by disease stage
- Censoring: Patients lost to follow-up or alive at study end
- Subgroups: Disease stage, ECOG performance status
Missing Data Plan:
- Up to 5% dropout acceptable before triggering sensitivity analyses
- Primary: Multiple imputation (chained equations)
- Sensitivity: Last observation carried forward (LOCF), worst-case imputation
What's Included
- Analysis Plan Development Workflow: Step-by-step guidance covering trial design assessment, statistical framework selection, and pre-specification of all analyses.
- Regulatory Compliance Checklist: Requirements aligned with FDA, EMA, ICH-GCP, and health authority-specific statistical guidance to ensure approvability.
- Statistical Power Calculator Reference: Formulas and assumptions for common trial designs (parallel, crossover, adaptive) with sensitivity tables for effect size variations.
- Sample Analysis Plan Template: Customizable SAP sections including study objectives, populations, variables, and statistical method specifications ready for protocol integration.
- Risk Assessment Framework: Identify statistical vulnerabilities (multiplicity, missing data, confounding) and pre-specify mitigation strategies to strengthen scientific evidence.
Who It's For
- Biostatisticians and statisticians designing clinical trials
- Clinical researchers writing study protocols and statistical sections
- Regulatory scientists preparing submissions to health authorities
- Pharmaceutical and biotechnology program managers ensuring analytical rigor
- Academic researchers conducting NIH-funded or investigator-initiated trials
Best For
- Designing statistical analysis plans for Phase II and Phase III trials
- Determining sample sizes and power calculations
- Planning subgroup and sensitivity analyses
- Specifying missing data and interim analysis strategies
- Documenting pre-specified analyses for regulatory submissions
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