bbssr 1.0.2

Minor Updates

• Fixed title case in DESCRIPTION file for CRAN submission
• Updated from “Re-estimation” to “Re-Estimation” as requested by CRAN

bbssr 1.0.1

Minor Updates

• Function Removal: Removed ClopperPearsonCI() function as it was not being used in the main BSSR functionality
• Documentation Updates: Updated all documentation to reflect the removal of confidence interval functionality
• Package Optimization: Streamlined package to focus on core BSSR methods

bbssr 1.0.0

Initial Release

This is the first release of bbssr, a comprehensive R package for blinded sample size re-estimation (BSSR) in two-arm clinical trials with binary endpoints.

Main Features

• Blinded Sample Size Re-estimation: Implement adaptive trial designs with BinaryPowerBSSR()
• Multiple Exact Statistical Tests: Support for 5 different exact tests:
  • Pearson chi-squared test ('Chisq')
  • Fisher exact test ('Fisher')
  • Fisher mid-p test ('Fisher-midP')
  • Z-pooled exact unconditional test ('Z-pool')
  • Boschloo exact unconditional test ('Boschloo')
• Flexible Design Options: Choose between restricted, unrestricted, and weighted BSSR approaches
• Traditional Methods: Calculate power (BinaryPower()) and sample sizes (BinarySampleSize()) for fixed designs
• Exact Confidence Intervals: Clopper-Pearson confidence intervals (ClopperPearsonCI())
• Rejection Regions: Compute exact rejection regions (BinaryRR())

Design Approaches

• Restricted Design: Conservative approach ensuring final sample size ≥ initial sample size
• Unrestricted Design: Flexible approach allowing both sample size increases and decreases
• Weighted Design: Advanced approach using weighted averaging across interim scenarios

Documentation

• Comprehensive documentation with examples for all functions
• Detailed vignettes explaining methodology and usage:
  • vignette("bbssr-introduction") - Getting started guide
  • vignette("bbssr-statistical-methods") - Statistical methodology
• Complete README with practical examples

Statistical Validity

• All methods maintain exact Type I error control at specified α level
• Exact statistical tests rather than asymptotic approximations
• Suitable for small to moderate sample sizes common in clinical trials

Dependencies

• Base R (≥ 3.5.0)
• fpCompare for robust floating-point comparisons
• stats for statistical functions

Development

• Package follows R package development best practices
• Comprehensive documentation with roxygen2
• Ready for CRAN submission