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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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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