{ "_id": "6ac917fdde46216d5edd97dc", "Package": "SimTOST", "Title": "Sample Size Estimation for Bio-Equivalence Trials Through\nSimulation", "Version": "1.1.0", "Authors@R": "c(\nperson(given = \"Thomas\",\nfamily = \"Debray\",\nemail = \"tdebray@fromdatatowisdom.com\",\nrole = c(\"aut\", \"cre\")),\nperson(given = \"Tim\",\nfamily = \"Friede\",\nemail = \"tim.friede@med.uni-goettingen.de\",\nrole = c(\"ctb\")),\nperson(given = \"Johanna\",\nfamily = \"Munoz\",\nemail = \"johanna.munoz@fromdatatowisdom.com\",\nrole = c(\"ctb\")),\nperson(given = \"Dewi\",\nfamily = \"Amaliah\",\nemail = \"dewi.amaliah@fromdatatowisdom.com\",\nrole = c(\"ctb\")),\nperson(given = \"Wei\",\nfamily = \"Wei\",\nemail = \"wei.wei@biogen.com\",\nrole = c(\"ctb\")),\nperson(given = \"Marian\",\nfamily = \"Mitroiu\",\nemail = \"marian.mitroiu@biogen.com\",\nrole = c(\"ctb\")),\nperson(given = \"Scott\",\nfamily = \"McDonald\",\nemail = \"scott.mcdonald@fromdatatowisdom.com\",\nrole = c(\"ctb\")),\nperson(\"Biogen Inc\",\nrole = c(\"cph\", \"fnd\"),\ncomment = \"Copyright holder and funder of the original implementation v1.0.0\"),\nperson(\"Smart Data Analysis and Statistics B.V.\",\nrole = c(\"cph\", \"fnd\"),\ncomment = \"Copyright holder and funder of subsequent developments\")\n)", "Description": "Sample size estimation for bio-equivalence trials is\nsupported through a simulation-based approach that extends the\nTwo One-Sided Tests (TOST) procedure. The methodology provides\nflexibility in hypothesis testing, accommodates multiple\ntreatment comparisons, and accounts for correlated endpoints.\nUsers can model complex trial scenarios, including parallel and\ncrossover designs, intra-subject variability, and different\nequivalence margins. Monte Carlo simulations enable accurate\nestimation of power and type I error rates, ensuring\nwell-calibrated study designs. The statistical framework builds\non established methods for equivalence testing and multiple\nhypothesis testing in bio-equivalence studies, as described in\nSchuirmann (1987) , Mielke et al.\n(2018) , Shieh (2022)\n, and Sozu et al. (2015)\n. Comprehensive documentation\nand vignettes guide users through implementation and\ninterpretation of results.", "License": "Apache License (>= 2)", "Encoding": "UTF-8", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "URL": "https://smartdata-analysis-and-statistics.github.io/SimTOST/,\nhttps://github.com/smartdata-analysis-and-statistics/SimTOST", "Config/roxygen2/version": "8.1.0", "BugReports": "https://github.com/smartdata-analysis-and-statistics/SimTOST/issues", "Repository": "https://smartdata-analysis-and-statistics.r-universe.dev", "Date/Publication": "2026-10-09 11:59:51 UTC", "RemoteUrl": "https://github.com/smartdata-analysis-and-statistics/simtost", "RemoteRef": "HEAD", "RemoteSha": "5118001c66fc1798ac208eb5cab5bcad30b673d8", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-09 16:28:17 UTC", "User": "root" }, "Author": "Thomas Debray [aut, cre],\nTim Friede [ctb],\nJohanna Munoz [ctb],\nDewi Amaliah [ctb],\nWei Wei [ctb],\nMarian Mitroiu [ctb],\nScott McDonald [ctb],\nBiogen Inc [cph, fnd] (Copyright holder and funder of the original\nimplementation v1.0.0),\nSmart Data Analysis and Statistics B.V. 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