Package: crt2power 1.2.2

Melody Owen

crt2power: Designing Cluster-Randomized Trials with Two Continuous Co-Primary Outcomes

Provides methods for powering cluster-randomized trials with two continuous co-primary outcomes using five key design techniques. Includes functions for calculating required sample size and statistical power. For more details on methodology, see Owen et al. (2025) <doi:10.1002/sim.70015>, Yang et al. (2022) <doi:10.1111/biom.13692>, Pocock et al. (1987) <doi:10.2307/2531989>, Vickerstaff et al. (2019) <doi:10.1186/s12874-019-0754-4>, and Li et al. (2020) <doi:10.1111/biom.13212>.

Authors:Melody Owen [aut, cre]

crt2power_1.2.2.tar.gz
crt2power_1.2.2.zip(r-4.7)crt2power_1.2.2.zip(r-4.6)crt2power_1.2.2.zip(r-4.5)
crt2power_1.2.2.tgz(r-4.6-any)crt2power_1.2.2.tgz(r-4.5-any)
crt2power_1.2.2.tar.gz(r-4.7-any)crt2power_1.2.2.tar.gz(r-4.6-any)
crt2power_1.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
crt2power/json (API)

# Install 'crt2power' in R:

install.packages('crt2power', repos = c('https://melodyaowen.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/melodyaowen/crt2power/issues

On CRAN:

3.00 score 2 scripts 190 downloads 17 exports 166 dependencies

Last updated from:b1c1437784. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	360
source / vignettes	OK	244
linux-release-x86_64	OK	362
macos-release-arm64	OK	361
macos-oldrel-arm64	OK	259
windows-devel	OK	339
windows-release	OK	304
windows-oldrel	OK	324
wasm-release	OK	147

Exports:calc_K_comb_outcome calc_K_conj_test calc_K_disj_2dftest calc_K_pval_adj calc_K_single_1dftest calc_m_comb_outcome calc_m_conj_test calc_m_disj_2dftest calc_m_pval_adj calc_m_single_1dftest calc_ncp_chi2 calc_pwr_comb_outcome calc_pwr_conj_test calc_pwr_disj_2dftest calc_pwr_pval_adj calc_pwr_single_1dftest run_crt2_design

Dependencies:askpass backports base64enc bit bit64 blob brew brio broom bslib cachem callr cellranger class cli clipr codetools commonmark conflicted cpp11 crayon credentials curl data.table DBI dbplyr desc devtools diffobj digest downlit dplyr dtplyr e1071 ellipsis evaluate fansi farver fastmap fontawesome forcats foreach fs gargle gdata generics gert ggplot2 gh gitcreds glue gmodels googledrive googlesheets4 gtable gtools haven highr hms htmltools htmlwidgets httpuv httr httr2 ids ini isoband iterators jquerylib jsonlite knitr labeling labelled later lattice lifecycle lubridate magrittr MASS Matrix memoise mime miniUI minqa mitools modelr mvtnorm nlme numDeriv openssl otel pak pillar pkgbuild pkgconfig pkgdown pkgload praise prettyunits processx profvis progress promises proxy ps purrr R6 ragg rappdirs rcmdcheck RColorBrewer Rcpp RcppArmadillo readr readxl rematch rematch2 reprex rlang rmarkdown rootSolve roxygen2 rprojroot rstudioapi rversions rvest S7 sass scales selectr sessioninfo shiny sourcetools stringi stringr survey survival sys systemfonts tableone testthat textshaping tibble tidyr tidyselect tidyverse timechange tinytex tzdb urlchecker usethis utf8 uuid vctrs viridisLite vroom waldo whisker withr xfun xml2 xopen xtable yaml zip zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Calculate required number of clusters per treatment group for a cluster-randomized trial with co-primary endpoints using a combined outcomes approach.	calc_K_comb_outcome
Calculate required number of clusters per treatment group for a cluster-randomized trial with co-primary endpoints using the conjunctive intersection-union test approach.	calc_K_conj_test
Calculate required number of clusters per treatment group for a cluster-randomized trial with co-primary endpoints using a disjunctive 2-DF test approach.	calc_K_disj_2dftest
Calculate required number of clusters per treatment group for a cluster-randomized trial with co-primary endpoints using three common p-value adjustment methods	calc_K_pval_adj
Calculate required number of clusters per treatment group for a cluster-randomized trial with co-primary endpoints using the single 1-DF combined test approach.	calc_K_single_1dftest
Calculate cluster size for a cluster-randomized trial with co-primary endpoints using a combined outcomes approach.	calc_m_comb_outcome
Calculate cluster size for a cluster-randomized trial with co-primary endpoints using the conjunctive intersection-union test approach.	calc_m_conj_test
Calculate cluster size for a cluster-randomized trial with co-primary endpoints using a disjunctive 2-DF test approach.	calc_m_disj_2dftest
Calculate cluster size for a cluster-randomized trial with co-primary endpoints using three common p-value adjustment methods	calc_m_pval_adj
Calculate cluster size for a cluster-randomized trial with co-primary endpoints using the single 1-DF combined test approach.	calc_m_single_1dftest
Find the non-centrality parameter corresponding to Type I error rate and statistical power	calc_ncp_chi2
Calculate statistical power for a cluster-randomized trial with co-primary endpoints using a combined outcomes approach.	calc_pwr_comb_outcome
Calculate statistical power for a cluster-randomized trial with co-primary endpoints using the conjunctive intersection-union test approach.	calc_pwr_conj_test
Calculate statistical power for a cluster-randomized trial with co-primary endpoints using a disjunctive 2-DF test approach.	calc_pwr_disj_2dftest
Calculate statistical power for a cluster-randomized trial with co-primary endpoints using three common p-value adjustment methods	calc_pwr_pval_adj
Calculate statistical power for a cluster-randomized trial with co-primary endpoints using the single 1-DF combined test approach.	calc_pwr_single_1dftest
Find study design output specifications based on all five CRT co-primary design methods.	run_crt2_design