Package: ssutil 1.0.0

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_1.0.0.tar.gz
ssutil_1.0.0.zip(r-4.7)ssutil_1.0.0.zip(r-4.6)ssutil_1.0.0.zip(r-4.5)
ssutil_1.0.0.tgz(r-4.6-any)ssutil_1.0.0.tgz(r-4.5-any)
ssutil_1.0.0.tar.gz(r-4.7-any)ssutil_1.0.0.tar.gz(r-4.6-any)
ssutil_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
ssutil/json (API)
NEWS

# 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:

5.07 score 13 scripts 173 downloads 19 exports 71 dependencies

Last updated from:80df74a114. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK169
source / vignettesOK242
linux-release-x86_64OK166
macos-release-arm64OK96
macos-oldrel-arm64OK108
windows-develOK124
windows-releaseOK158
windows-oldrelOK149
wasm-releaseOK122

Exports:empirical_power_resultis.empirical_power_resultmultpmultzpower_best_binomialpower_best_normalpower_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:backportsbase64encbigDbitopsbroombslibcachemclicommonmarkcpp11curldigestdplyrevaluatefarverfastmapfontawesomefsgenericsggplot2gluegsDesigngtgtablehighrhtmltoolshtmlwidgetsisobandjquerylibjsonlitejuicyjuiceknitrlabelinglifecyclelitedownmagrittrmarkdownMASSmemoisemimemvtnormpillarpkgconfigpurrrr2rtfR6rappdirsRColorBrewerRcppreactablereactRrlangrmarkdownS7sassscalesstringistringrtibbletidyrtidyselecttinytexutf8V8vctrsviridisLitewithrxfunxml2xtableyaml

Detectable Event Rate for Safety Signal Detection: Using power_single_rate

Rendered frompower_single_rate.Rmdusingknitr::rmarkdownon May 16 2026.

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

Equivalence Testing for Normal Outcomes

Rendered fromequivalence.Rmdusingknitr::rmarkdownon May 16 2026.

Last update: 2025-06-03
Started: 2025-06-01

Selecting the best group using the Indifferent-Zone approach for binomial outcomes

Rendered fromiz_binomial.Rmdusingknitr::rmarkdownon May 16 2026.

Last update: 2025-05-29
Started: 2025-05-28

Selecting the best group using the Indifferent-Zone approach for normal outcomes

Rendered fromiz_normal.Rmdusingknitr::rmarkdownon May 16 2026.

Last update: 2025-05-29
Started: 2025-05-29

Simulations for Negative Binomial outcomes

Rendered fromnegative_binomial.Rmdusingknitr::rmarkdownon May 16 2026.

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

Simulations for Non-inferiority trials

Rendered fromnon_inferiority.Rmdusingknitr::rmarkdownon May 16 2026.

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

Readme and manuals

Help Manual

Help pageTopics
Create an Empirical Power Result objectempirical_power_result
Format method for power_single_rate classformat.power_single_rate
Check if an object is a sim_power_resultis.empirical_power_result
Calculate the Multivariate Normal Probabilitymultp
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
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_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