Advanced Adaptive Trial Design in R

关于

This skill provides specialized guidance for implementing advanced clinical trial methodologies in R, covering platform trials, basket and umbrella designs, and multi-arm multi-stage (MAMS) trials. It empowers biostatisticians and data scientists to perform interim analyses, response-adaptive randomization, and sample size re-estimation using robust packages like rpact, RBesT, and adaptr. Whether integrating historical data via Bayesian priors or managing multiplicity with graphical methods, this skill ensures best practices in modern clinical research design and simulation.

主要功能

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• Simulate platform trials with dynamic arm addition and dropping rules using adaptr
• Design Multi-Arm Multi-Stage (MAMS) trials for continuous and binary outcomes
• Implement response-adaptive randomization using Thompson sampling and constraints
• Perform sample size re-estimation and interim analyses for group sequential designs
• Analyze basket and umbrella trials with Bayesian hierarchical and MEM models

使用场景

• Integrating historical clinical data into current trials using Bayesian Meta-Analytic Predictive (MAP) priors
• Conducting interim analyses to determine if a trial should stop early or requires a sample size adjustment based on conditional power
• Designing a platform trial with multiple treatment arms and early stopping rules for futility or superiority