Package: cvcrand 0.1.0
cvcrand: Efficient Design and Analysis of Cluster Randomized Trials
Constrained randomization by Raab and Butcher (2001) <doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C> is suitable for cluster randomized trials (CRTs) with a small number of clusters (e.g., 20 or fewer). The procedure of constrained randomization is based on the baseline values of some cluster-level covariates specified. The intervention effect on the individual outcome can then be analyzed through clustered permutation test introduced by Gail, et al. (1996) <doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>. Motivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the package performs constrained randomization on the baseline values of cluster-level covariates and clustered permutation test on the individual-level outcomes for cluster randomized trials.
Authors:
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cvcrand.pdf |cvcrand.html✨
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NEWS
# Install 'cvcrand' in R: |
install.packages('cvcrand', repos = c('https://hengshiyu.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/hengshiyu/cvcrand/issues
- Dickinson_design - Raw county-level variables for study 1 in Dickinson et al
- Dickinson_outcome - Simulated individual-level binary outcome and baseline variables for study 1 in Dickinson et al
Last updated 1 years agofrom:4ed1c9b6ab. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 09 2024 |
R-4.5-win | NOTE | Nov 09 2024 |
R-4.5-linux | NOTE | Nov 09 2024 |
R-4.4-win | NOTE | Nov 09 2024 |
R-4.4-mac | NOTE | Nov 09 2024 |
R-4.3-win | NOTE | Nov 09 2024 |
R-4.3-mac | NOTE | Nov 09 2024 |
Dependencies:bitbit64classclicliprcpp11crayonDBIdplyre1071fansiforcatsgdatagenericsgluegmodelsgtoolshavenhmslabelledlatticelifecyclemagrittrMASSMatrixminqamitoolsnlmenumDerivpillarpkgconfigprettyunitsprogressproxypurrrR6RcppRcppArmadilloreadrrlangstringistringrsurveysurvivaltableonetibbletidyrtidyselecttzdbutf8vctrsvroomwithrzoo