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ssutil

ssutil is an R package providing a suite of tools for sample size estimation and power simulation in a variety of clinical trial designs. It includes methods for binomial, normal, and negative binomial endpoints, as well as support for equivalence and non-inferiority testing scenarios. It also includes functionality for power and sample size calculation for selecting the best group using the indifferent-zone approach for normal and binomial outcomes.

Features

  • Empirical power simulation for:
    • Binomial, normal, and negative binomial endpoints
    • Equivalence and non-inferiority designs
    • Best group selection using the indifferent-zone approach for normal and binomial outcomes
  • Sample size utilities for various design types

Installation

# Install from GitHub (requires remotes or devtools)
remotes::install_github("johnaponte/ssutil")

Vignettes

Learn how to use ssutil through these worked examples:

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Version

Install

install.packages('ssutil')

Monthly Downloads

120

Version

1.0.0

License

AGPL (>= 3)

Issues

Pull Requests

Stars

Forks

Maintainer

Aponte John

Last Published

June 12th, 2025

Functions in ssutil (1.0.0)

sim_power_best_norm_rank

Simulate Power to Select Best Group by Ranks (Normal Outcomes)
ss_best_normal

Sample Size for Selecting the Best Treatment in a Normal Response (Indifference-Zone)
sim_power_best_bin_rank

Simulate Power to Rank the Best Group Using Binomial Outcomes
prophr

Calculate Event Probability in the Experimental Group Given a Hazard Ratio
ss_best_binomial

Sample Size to Select the Best Group in a Binomial Test
wcs_power_best_binomial

Worst‐Case Scenario Power for the Best Binomial Group
sim_power_best_normal

Simulate Power to Select Best Group (Normal Outcomes)
ss_ni_ve

Sample Size and Non-Inferiority Margin for Vaccine Efficacy Trials
sim_power_equivalence_normal

Empirical Power for Equivalence (Normal Outcomes)
ssutil-package

ssutil: Utilities for Sample Size calculation
power_best_binomial

Power to Correctly Select the Best Group in a Binomial Test
format.power_single_rate

Format method for power_single_rate class
multp

Calculate the Multivariate Normal Probability
power_best_normal

Power calculation for the Indifferent-zone approach for normal outcomes
print.empirical_power_result

Print method for empirical_power_result
is.empirical_power_result

Check if an object is a sim_power_result
power_single_rate

Detectable Event Rate with Specified Power and Sample Size
multz

Calculate the Upper Equicoordinate Point of a Multivariate Normal Distribution
empirical_power_result

Create an Empirical Power Result object
print.power_single_rate

Print method for class power_single_rate
sim_power_best_binomial

Simulate Power to Select the Best Group Using Binomial Outcomes
sim_power_nbinom

Empirical Power for Negative Binomial Comparison
sim_power_ni_normal

Empirical Power for Non-Inferiority (Normal Outcomes)
tidy.empirical_power_result

Tidy Method for empirical_power_result