Does this support adaptive trial designs?

Yes, it provides methods for Group Sequential Designs and adaptive spending functions using the gsDesign and rpact packages.

Which R packages does this skill utilize?

It leverages industry-standard packages including pwr, gsDesign, rpact, blockrand, gMCP, mmrm, mice, and consort for comprehensive clinical analysis.

Is this skill compliant with regulatory standards?

It includes implementation patterns for ICH E9 estimands and generates CONSORT-compliant diagrams to support regulatory-grade documentation and reporting.

How does it handle missing data in clinical datasets?

The skill implements Mixed Models for Repeated Measures (MMRM), Multiple Imputation (MICE), and sensitivity analyses like Tipping Point analysis for MNAR data.

Clinical Trials Statistical Analysis

Name: Clinical Trials Statistical Analysis
Author: choxos

bychoxos

Data Science & ML

Provides R-based statistical methods and best practices for clinical trial design, analysis, and regulatory reporting.

About

This skill equips Claude with specialized expertise in clinical trial methodology using R. It covers the entire trial lifecycle—from sample size calculation and randomization strategies (simple, stratified, minimization) to advanced analysis techniques like Group Sequential Designs, multiplicity adjustments, and MMRM for missing data. It facilitates regulatory compliance by incorporating ICH E9 estimand frameworks and CONSORT diagram generation, making it an essential tool for biostatisticians and clinical data scientists working with the Tidyverse and specialized R packages like gsDesign, rpact, and mmrm.

Key Features

Robust missing data handling via MMRM, Multiple Imputation, and Tipping Point analysis
Automated sample size calculations for t-tests, proportions, and survival endpoints
Regulatory-ready outputs including CONSORT diagrams and ICH E9 estimand frameworks
Group sequential design implementation with O'Brien-Fleming and Pocock boundaries
0 GitHub stars
Advanced randomization schemes including stratified block and minimization techniques

Use Cases

Designing Phase II/III clinical trials with complex interim analysis looks and alpha spending
Generating production-grade statistical analysis plans and visualizations for regulatory submissions
Implementing multiplicity adjustments and graphical approaches for multiple primary and secondary endpoints

About

Key Features

Robust missing data handling via MMRM, Multiple Imputation, and Tipping Point analysis
Automated sample size calculations for t-tests, proportions, and survival endpoints
Regulatory-ready outputs including CONSORT diagrams and ICH E9 estimand frameworks
Group sequential design implementation with O'Brien-Fleming and Pocock boundaries
0 GitHub stars
Advanced randomization schemes including stratified block and minimization techniques

Use Cases

Designing Phase II/III clinical trials with complex interim analysis looks and alpha spending
Generating production-grade statistical analysis plans and visualizations for regulatory submissions
Implementing multiplicity adjustments and graphical approaches for multiple primary and secondary endpoints