Package: ssutil 1.2.0

ssutil: Sample Size Calculation Tools

Functions for sample size estimation and simulation in clinical trials. Includes methods for selecting the best group using the Indifference-zone approach, as well as designs for non-inferiority, equivalence, and negative binomial models. For the sample size calculation for non-inferiority of vaccines, the approach is based on Fleming, Powers, and Huang (2021) <doi:10.1177/1740774520988244>. The Indifference-zone approach is based on Sobel and Huyett (1957) <doi:10.1002/j.1538-7305.1957.tb02411.x> and Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9).

Authors:John J. Aponte [aut, cre], Chris Gast [ctb]

ssutil_1.2.0.tar.gz
ssutil_1.2.0.zip(r-4.7-any)ssutil_1.2.0.zip(r-4.6-any)ssutil_1.2.0.zip(r-4.5-any)
ssutil_1.2.0.tgz(r-4.6-any)ssutil_1.2.0.tgz(r-4.5-any)
ssutil_1.2.0.tar.gz(r-4.7-any)ssutil_1.2.0.tar.gz(r-4.6-any)
ssutil_1.2.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ssutil/json (API)

# Install 'ssutil' in R:
install.packages('ssutil', repos = c('https://johnaponte.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/johnaponte/ssutil/issues

Pkgdown/docs site:https://johnaponte.github.io

On CRAN:

Conda:

5.06 score 19 scripts 317 downloads 20 exports 24 dependencies

Last updated from:765669b0c9. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK166
source / vignettesOK207
linux-releaseOK159
macos-releaseOK95
macos-oldrelOK86
windows-develOK127
windows-releaseOK101
windows-oldrelOK104
wasm-releaseOK111

Exports:empirical_power_resultis.empirical_power_resultmultpmultzpower_best_binomialpower_best_normalpower_events_ratepower_single_rateprophrsim_power_best_bin_ranksim_power_best_binomialsim_power_best_norm_ranksim_power_best_normalsim_power_equivalence_normalsim_power_nbinomsim_power_ni_normalss_best_binomialss_best_normalss_ni_vewcs_power_best_binomial

Dependencies:backportsbroomclicpp11dplyrgenericsgluelifecyclemagrittrMASSmvtnormpillarpkgconfigpurrrR6rlangstringistringrtibbletidyrtidyselectutf8vctrswithr

Simulations for Non-inferiority trials
Introduction | Example
Rendered fromnon_inferiority.Rmdusingknitr::rmarkdown

Last update: 2026-09-17
Started: 2025-06-03

Equivalence Testing for Normal Outcomes
Introduction | Example: Equivalence Among Vaccine Lots
Rendered fromequivalence.Rmdusingknitr::rmarkdown

Last update: 2026-09-06
Started: 2025-06-01

Power for rare events
Introduction | power_single_rate() | Example 1: 100 Subjects, Power 0.95 | Example 2: 30 Subjects, Power 0.95, 0.90, and 0.8 | Example 3: 500 and 1500 Subjects | power_events_rate() | Example 4: 500 and 1500 Subjects Across Standard Frequency Categories | References
Rendered frompower_rare_events.Rmdusingknitr::rmarkdown

Last update: 2026-09-06
Started: 2026-09-06

Selecting the best group using the Indifference-Zone approach for binomial outcomes
Introduction | Examples with a single outcome | Examples using multiple outcomes | References
Rendered fromiz_binomial.Rmdusingknitr::rmarkdown

Last update: 2026-09-06
Started: 2025-05-28

Selecting the best group using the Indifference-Zone approach for normal outcomes
Introduction | Examples with a single outcome | Examples using multiple outcomes | References
Rendered fromiz_normal.Rmdusingknitr::rmarkdown

Last update: 2026-09-06
Started: 2025-05-29

Simulations for Negative Binomial outcomes
Introduction | Example
Rendered fromnegative_binomial.Rmdusingknitr::rmarkdown

Last update: 2026-09-06
Started: 2025-06-03

Readme and manuals

Help Manual

Help pageTopics
Create an Empirical Power Result objectempirical_power_result
Format method for power_events_rate classformat.power_events_rate
Format method for power_single_rate classformat.power_single_rate
Check if an object is a sim_power_resultis.empirical_power_result
Multivariate Normal Equicoordinate Cumulative Distribution Functionmultp
Calculate the Upper Equicoordinate Point of a Multivariate Normal Distributionmultz
Power to Correctly Select the Best Group in a Binomial Testpower_best_binomial
Power calculation for the Indifferent-zone approach for normal outcomespower_best_normal
Probability of Observing At Least a Given Number of Eventspower_events_rate
Detectable Event Rate with Specified Power and Sample Sizepower_single_rate
Print method for empirical_power_resultprint.empirical_power_result
Print method for class power_events_rateprint.power_events_rate
Print method for class power_single_rateprint.power_single_rate
Calculate Event Probability in the Experimental Group Given a Hazard Ratioprophr
Simulate Power to Rank the Best Group Using Binomial Outcomessim_power_best_bin_rank
Simulate Power to Select the Best Group Using Binomial Outcomessim_power_best_binomial
Simulate Power to Select Best Group by Ranks (Normal Outcomes)sim_power_best_norm_rank
Simulate Power to Select Best Group (Normal Outcomes)sim_power_best_normal
Empirical Power for Equivalence (Normal Outcomes)sim_power_equivalence_normal
Empirical Power for Negative Binomial Comparisonsim_power_nbinom
Empirical Power for Non-Inferiority (Normal Outcomes)sim_power_ni_normal
Sample Size to Select the Best Group in a Binomial Testss_best_binomial
Sample Size for Selecting the Best Treatment in a Normal Response (Indifference-Zone)ss_best_normal
Sample Size and Non-Inferiority Margin for Vaccine Efficacy Trialsss_ni_ve
Tidy Method for empirical_power_resulttidy.empirical_power_result
Worst‐Case Scenario Power for the Best Binomial Groupwcs_power_best_binomial