BayesianPredictiveFutility

BayesianPredictiveFutility performs interim futility analysis for single-arm phase II clinical trials using a Bayesian predictive probability design to inform early stopping decisions.

Key Features:

• Bayesian Framework: Employs posterior and predictive probabilities to guide trial decision-making based on accumulating data.
• Interim Analysis Plan: Calculates stopping boundaries for futility at interim stages using a Bayesian predictive design.
• Predictive Probability Evaluation: Assesses predictive probabilities at interim analyses to estimate the likelihood of future trial success.
• Pre-defined Sample Sizes: Uses predefined sample sizes to determine the minimum number of responders required to claim efficacy at study completion.
• Statistical Plan Generation: Produces summaries, study design specifics, tables, figures, and performance metrics including probability of early termination (PET), type I error rates, and power.
• Sensitivity Analysis: Includes sensitivity analyses for predictive probability, posterior probability, sample size, and beta prior distribution.
• Output Formats: Exports results in Word or PDF formats.

Scientific Applications:

• Single-Arm Phase II Trials: Enables interim futility decisions to conserve resources and redirect efforts during single-arm phase II studies.
• Early-Phase Clinical Research: Supports decision-making in early-phase clinical research through predictive probability assessment.
• Lung Cancer Clinical Trials: Has been demonstrated in clinical trials in lung cancer to inform interim futility assessments.

Methodology:

Implements a Bayesian predictive probability design using predefined sample sizes to compute minimum responder thresholds, evaluates predictive probabilities at interim stages to inform stopping rules, and performs sensitivity analyses for predictive probability, posterior probability, sample size, and beta prior distribution.