Does this skill support group sequential designs?

Yes, it includes implementation methods for O'Brien-Fleming and Pocock boundaries using industry-standard R packages like gsDesign and rpact.

Can I use this for handling missing data in clinical trials?

Absolutely, it provides established patterns for MMRM (Mixed Models for Repeated Measures) and multiple imputation using the mice and rbmi packages.

What randomization techniques are included?

The skill covers simple randomization, stratified block randomization, and minimization techniques using the blockrand and Minirand packages.

Is this skill aligned with regulatory standards?

The skill incorporates methods aligned with the ICH E9 estimands framework and provides tools for generating CONSORT diagrams for transparent reporting.

Clinical Trials Statistical Methods

Name: Clinical Trials Statistical Methods
Author: choxos

bychoxos

Ciencia de Datos y ML

Performs comprehensive clinical trial design and statistical analysis in R, covering sample size calculation, randomization, and regulatory-compliant modeling.

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This skill provides a specialized framework for clinical researchers and statisticians to execute rigorous statistical workflows within R. It streamlines complex tasks such as power analysis for various endpoints, implementation of advanced randomization strategies (including stratified block and minimization), and the design of group sequential trials with alpha spending functions. Additionally, it offers robust patterns for handling missing data via MMRM and multiple imputation, as well as multiplicity adjustments and CONSORT diagram generation, ensuring that trial analyses meet ICH E9 regulatory standards.

Características Principales

Sample size calculation for means, proportions, and survival endpoints
Advanced randomization including stratified block and minimization techniques
Group sequential design with O'Brien-Fleming and Pocock boundaries
Regulatory-grade missing data analysis using MMRM and multiple imputation
Multiplicity adjustments and graphical gatekeeping procedures
0 GitHub stars

Casos de Uso

Designing Phase II/III trials with group sequential interim monitoring and efficacy boundaries
Implementing complex randomization schemes for multi-center clinical studies
Conducting primary efficacy analyses and sensitivity tests for regulatory submissions

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Características Principales

Sample size calculation for means, proportions, and survival endpoints
Advanced randomization including stratified block and minimization techniques
Group sequential design with O'Brien-Fleming and Pocock boundaries
Regulatory-grade missing data analysis using MMRM and multiple imputation
Multiplicity adjustments and graphical gatekeeping procedures
0 GitHub stars

Casos de Uso

Designing Phase II/III trials with group sequential interim monitoring and efficacy boundaries
Implementing complex randomization schemes for multi-center clinical studies
Conducting primary efficacy analyses and sensitivity tests for regulatory submissions