SMARTAR: Sequential Multiple Assignment Randomized Trial and Adaptive Randomization

Primary data analysis for sequential multiple assignment randomization trial (SMART) and calibration tools for clinical trial planning purposes. \n The methods used for this package include: \n (1) Likelihood-based global test (hypothesis test, power calculation) by in Zhong X., Cheng, B., Qian M., Cheung Y.K. (2019) <doi:10.1016/j.cct.2019.105830>. \n (2) IPWE-based global test (hypotehsis test, power calculation) by Ogbagaber S.B., Karp J., Wahed A.S. (2016) <doi:10.1002/sim.6747>. \n (3) G estimates (pairwise comparison, power calculation) by Lavori R., Dawson P.W. (2012) <doi:10.1093/biostatistics/kxr016>. \n (4) IPW estimates (pairwise comparison, power calculation) by Murphy S.A. (2005) <doi:10.1002/sim.2022>. \n (5) SAMRT with adaptive randomization by Cheung Y.K. (2015) <doi:10.1111/biom.12258>.

Version: 1.1.0
Depends: R (≥ 3.5.0)
Imports: graphics, MASS, stats
Suggests: knitr, rmarkdown, testthat
Published: 2020-07-30
Author: Tony Zhong \n Xinru Wang \n Bin Cheng \n Ying Kuen Cheung
Maintainer: Tony Zhong <xiaobo.zhong at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: SMARTAR results


Reference manual: SMARTAR.pdf
Vignettes: SMARTAR-tutorial
Package source: SMARTAR_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: SMARTAR_1.1.0.tgz, r-oldrel: SMARTAR_1.1.0.tgz
Old sources: SMARTAR archive


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