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  "Title": "Efficient Design and Analysis of Cluster Randomized Trials",
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  "Authors@R": "c(person(given = \"Hengshi\", family = \"Yu\", role = c(\"aut\", \"cre\"), \nemail=\"hengshi@umich.edu\"),\nperson(given = \"Fan\", family = \"Li\", role =\"aut\",\nemail = \"fan.f.li@yale.edu\"),\nperson(given = \"John A.\", family = \"Gallis\", role =\"aut\",\nemail = \"john.gallis@duke.edu\"),\nperson(given = \"Elizabeth L.\", family = \"Turner\", role =\"aut\",\nemail = \"liz.turner@duke.edu\"))",
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  "Description": "Constrained randomization by Raab and Butcher (2001)\n<doi:10.1002/1097-0258(20010215)20:3%3C351::AID-SIM797%3E3.0.CO;2-C>\nis suitable for cluster randomized trials (CRTs) with a small\nnumber of clusters (e.g., 20 or fewer). The procedure of\nconstrained randomization is based on the baseline values of\nsome cluster-level covariates specified. The intervention\neffect on the individual outcome can then be analyzed through\nclustered permutation test introduced by Gail, et al. (1996)\n<doi:10.1002/(SICI)1097-0258(19960615)15:11%3C1069::AID-SIM220%3E3.0.CO;2-Q>.\nMotivated from Li, et al. (2016) <doi:10.1002/sim.7410>, the\npackage performs constrained randomization on the baseline\nvalues of cluster-level covariates and clustered permutation\ntest on the individual-level outcomes for cluster randomized\ntrials.",
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