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  "Title": "Randomization-Based Covariance Adjustment of Win Statistics",
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  "Authors@R": "c(person(\"Ann Marie K.\", \"Weideman\", email = \"anndo1@umbc.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Elaine K.\", \"Kowalewski\", role = \"aut\"),\nperson(\"Gary G.\", \"Koch\", role = \"aut\"))",
  "Author": "Ann Marie K. Weideman [aut, cre], Elaine K. Kowalewski [aut],\nGary G. Koch [aut]",
  "Maintainer": "Ann Marie K. Weideman <anndo1@umbc.edu>",
  "Description": "A multi-visit clinical trial may collect participant\nresponses on an ordinal scale and may utilize a stratified\ndesign, such as randomization within centers, to assess\ntreatment efficacy across multiple visits. Baseline\ncharacteristics may be strongly associated with the outcome,\nand adjustment for them can improve power. The win ratio\n(ignores ties) and the win odds (accounts for ties) can be\nuseful when analyzing these types of data from randomized\ncontrolled trials. This package provides straightforward\nfunctions for adjustment of the win ratio and win odds for\nstratification and baseline covariates, facilitating the\ncomparison of test and control treatments in multi-visit\nclinical trials. For additional information concerning the\nmethodologies and applied examples within this package, please\nrefer to the following publications: 1. Weideman, A.M.K.,\nKowalewski, E.K., & Koch, G.G. (2024). “Randomization-based\ncovariance adjustment of win ratios and win odds for randomized\nmulti-visit studies with ordinal outcomes.” Journal of\nStatistical Research, 58(1), 33–48.\n<doi:10.3329/jsr.v58i1.75411>. 2. Kowalewski, E.K., Weideman,\nA.M.K., & Koch, G.G. (2023). “SAS macro for randomization-based\nmethods for covariance and stratified adjustment of win ratios\nand win odds for ordinal outcomes.” SESUG 2023 Proceedings,\nPaper 139-2023.",
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