Bayesian Hybrid Design and Analysis
Statistical Analysis for a Bayesian Hybrid Design
Calculate Borrowing Weights from Historical Data
Calibration for Bayesian Hybrid Design
Analysis of a Study Using Bayesian Hybrid Design using Dynamic Power P...
Bayesian Hybrid Design using Dynamic Power Prior Framework
Bayesian Hybrid Design
Clopper-Pearson Exact Confidence Interval for a Binomial Proportion
Explore Power Across Multiple Scenarios for a Bayesian Hybrid Design
Calculate Rejection Boundary for Fisher's Exact Test
Power Calculation by Fisher's Exact Test
Fisher's Exact Test for a 2x2 Contingency Table
Statistical Analysis Using Frequentist Method
Plot a DPP Object
Plot Posterior Mean Difference (PMD) Distributions
Power Calculation for the Exact Binomial Test
Power Calculation for Bayesian Hybrid Design
Run a Calibrated Simulation Using SAM Priors
Generating Operating Characteristics of SAM Priors
Calculating SAM priors
Calculating Mixture Weight of SAM Priors
Support of Distributions
Sample Size for Comparing Two Proportions
Implements Bayesian hybrid designs that incorporate historical control data into a current clinical trial. The package uses a dynamic power prior method to determine the degree of borrowing from the historical data, creating a 'hybrid' control arm. This approach is primarily designed for studies with a binary primary endpoint, such as the overall response rate (ORR). Functions are provided for design calibration, sample size calculation, power evaluation, and final analysis. Additionally, it includes functions adapted from the 'SAMprior' package (v1.1.1) by Yang et al. (2023) <https://academic.oup.com/biometrics/article/79/4/2857/7587575> to support the Self-Adapting Mixture (SAM) prior framework for comparison.