Author: Tony Zhong <xiaobo.zhong@mountsinai.org>  \n
Description: 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>.

Title: Bayesian Set of Best Dynamic Treatment Regimes and Sample Size
Authors@R: person("William", "Artman", email = "William_Artman@URMC.Rochester.edu", role = c("aut", "cre"))
Description: Permits determination of a set of
optimal dynamic treatment regimes and sample size for SMART design
in the Bayesian setting. Please see Artman (2020) <arXiv:2008.02341>.
Suggests: knitr, rmarkdown (>= 2.1), data.table

optimal dynamic treatment regimes and sample size for a SMART design

in the Bayesian setting with binary outcomes. Please see Artman (2020) <arXiv:2008.02341>.

Title: Sample Size for SMART Designs in Non-Surgical Periodontal Trials
Author: Jing Xu, Dipankar Bandyopadhyay, Douglas Azevedo, Bibhas Chakraborty
Description: Sample size calculation to detect dynamic treatment regime (DTR) effects based on change in clinical attachment level (CAL) outcomes from a non-surgical chronic periodontitis treatments study. The experiment is performed under a Sequential Multiple Assignment Randomized Trial (SMART) design. The clustered tooth (sub-unit) level CAL outcomes are skewed, spatially-referenced, and non-randomly missing. The implemented algorithm is available in Xu et al. (2019+) <arXiv:1902.09386>.
Imports: covr, sn (>= 1.5), mvtnorm (>= 1.0), stats, methods