Package: rPowerSampleSize 1.0.2

rPowerSampleSize: Sample Size Computations Controlling the Type-II Generalized Family-Wise Error Rate

The significance of mean difference tests in clinical trials is established if at least r null hypotheses are rejected among m that are simultaneously tested. This package enables one to compute necessary sample sizes for single-step (Bonferroni) and step-wise procedures (Holm and Hochberg). These three procedures control the q-generalized family-wise error rate (probability of making at least q false rejections). Sample size is computed (for these single-step and step-wise procedures) in a such a way that the r-power (probability of rejecting at least r false null hypotheses, i.e. at least r significant endpoints among m) is above some given threshold, in the context of tests of difference of means for two groups of continuous endpoints (variables). Various types of structure of correlation are considered. It is also possible to analyse data (i.e., actually test difference in means) when these are available. The case r equals 1 is treated in separate functions that were used in Lafaye de Micheaux et al. (2014) <doi:10.1080/10543406.2013.860156>.

Authors:Pierre Lafaye de Micheaux, Benoit Liquet and Jeremie Riou

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rPowerSampleSize.pdf |rPowerSampleSize.html
rPowerSampleSize/json (API)

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

Peer review:

Datasets:
  • data - Simulated data

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.00 score 9 scripts 150 downloads 15 exports 2 dependencies

Last updated 7 years agofrom:25affa5673. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 20 2024
R-4.5-winOKNov 20 2024
R-4.5-linuxOKNov 20 2024
R-4.4-winOKNov 20 2024
R-4.4-macOKNov 20 2024
R-4.3-winOKNov 20 2024
R-4.3-macOKNov 20 2024

Exports:bonferroni.1m.ssccomplexitydf.computeglobal.1m.analysisglobal.1m.sscindiv.1m.analysisindiv.1m.sscindiv.analysisindiv.rm.sscmatrix.type.computemontecarloplot.rPowerPsirmdPsirmsPsirmu

Dependencies:mvtnormssanv

Readme and manuals

Help Manual

Help pageTopics
Sample Size Computation and Data Analysis in the context of multiple continuous endpoints in clinical trials.rPowerSampleSize-package rPowerSampleSize
Sample Size Computation with Single Step Bonferroni Method in the Context of Multiple Continuous Endpoints.bonferroni.1m.ssc
Computation of the complexity of the numerical computations.complexity
Simulated datadata data.sim
Computation of degrees of freedom.df.compute
Data analysis with a global method in the context of multiple continuous endpointsglobal.1m.analysis
Sample Size Computation Based on a Global Procedure in the Context of Multiple Continuous Endpointsglobal.1m.ssc
Data analysis with an individual testing procedure in the context of multiple continuous endpointsfwer indiv.1m.analysis test.indiv
Sample size computation with an individual testing procedure in the context of multiple continuous endpointsasympt.ssc indiv.1m.ssc known.ssc unknown.ssc
Data analysis using an individual testing procedure controlling the q-gFWER in the context of m multiple continuous endpointsindiv.analysis
Sample size determination in the context of multiple continuous endpoints with a control of the q-gFWER, for a given value of r-power (generalized disjunctive power).indiv.rm.ssc
Computation of matrix typematrix.type.compute
Monte carlo computation of power.montecarlo
Plot of an rPower objectplot.rPower
Computation of power for step-down (Holm) procedure.Psirmd
Computation of power for single step (Bonferroni) procedure.Psirms
Computation of power for step-up (Hochberg) procedure.Psirmu