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gsDesignNB

gsDesignNB provides fixed design and group sequential design simulation for recurrent event scenarios to be analyzed as a Poisson process or negative binomial model. For group sequential design, the package can be easily used with the gsDesign package. Key is the computation of statistical information at the time of analysis.

Installation

You can install gsDesignNB from CRAN with:

install.packages("gsDesignNB")

Or install the development version from GitHub with:

remotes::install_github("keaven/gsDesignNB")

Code style

This package follows the tidyverse style guide.

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Version

Install

install.packages('gsDesignNB')

Version

0.2.6

License

GPL (>= 3)

Issues

0

Pull Requests

0

Stars

1

Forks

1

Maintainer

Keaven Anderson

Last Published

February 16th, 2026

Functions in gsDesignNB (0.2.6)

summarize_gs_sim

Summarize group sequential simulation results
summary.sample_size_nbinom_result

Summary for sample_size_nbinom_result objects
reexports

Objects exported from other packages
toInteger

Convert group sequential design to integer sample sizes
summary.gsNB

Summary for gsNB objects
unblinded_ssr

Unblinded sample size re-estimation for recurrent events
cut_data_by_date

Cut simulated trial data at a calendar date
cut_date_for_completers

Find calendar date for target completer count
get_analysis_date

Find calendar date for target event count
cut_completers

Cut data for completers analysis
check_gs_bound

Check group sequential bounds
blinded_ssr

Blinded sample size re-estimation for recurrent events
calculate_blinded_info

Calculate blinded statistical information
estimate_nb_mom

Method of Moments Estimation for Negative Binomial Parameters
compute_info_at_time

Compute statistical information at analysis time
get_cut_date

Determine analysis date based on criteria
gsNBCalendar

Group sequential design for negative binomial outcomes
mutze_test

Wald test for treatment effect using negative binomial model (Mutze et al.)
sample_size_nbinom

Sample size calculation for negative binomial distribution
print.gsNBsummary

Print method for gsNBsummary objects
print.sample_size_nbinom_result

Print method for sample_size_nbinom_result objects
nb_sim

Simulate recurrent events with fixed follow-up
sim_gs_nbinom

Simulate group sequential clinical trial for negative binomial outcomes
nb_sim_seasonal

Simulate recurrent events with seasonal rates
print.sample_size_nbinom_summary

Print method for sample_size_nbinom_summary objects