Package: SimTOST
Title: Sample Size Estimation for Bio-Equivalence Trials Through
        Simulation
Version: 1.1.0
Authors@R: c(
    person(given = "Thomas", 
           family = "Debray", 
           email = "tdebray@fromdatatowisdom.com", 
           role = c("aut", "cre")),
    person(given = "Tim",
           family = "Friede",
           email = "tim.friede@med.uni-goettingen.de",
           role = c("ctb")),
    person(given = "Johanna", 
           family = "Munoz", 
           email = "johanna.munoz@fromdatatowisdom.com", 
           role = c("ctb")),
    person(given = "Dewi",
           family = "Amaliah",
           email = "dewi.amaliah@fromdatatowisdom.com",
           role = c("ctb")),
    person(given = "Wei",
           family = "Wei",
           email = "wei.wei@biogen.com",
           role = c("ctb")),
    person(given = "Marian",
           family = "Mitroiu",
           email = "marian.mitroiu@biogen.com",
           role = c("ctb")),
    person(given = "Scott",
           family = "McDonald",
           email = "scott.mcdonald@fromdatatowisdom.com",
           role = c("ctb")),
    person("Biogen Inc",
           role = c("cph", "fnd"),
           comment = "Copyright holder and funder of the original implementation v1.0.0"),
    person("Smart Data Analysis and Statistics B.V.",
           role = c("cph", "fnd"),
           comment = "Copyright holder and funder of subsequent developments")
    )
Description: Sample size estimation for bio-equivalence trials is
        supported through a simulation-based approach that extends the
        Two One-Sided Tests (TOST) procedure. The methodology provides
        flexibility in hypothesis testing, accommodates multiple
        treatment comparisons, and accounts for correlated endpoints.
        Users can model complex trial scenarios, including parallel and
        crossover designs, intra-subject variability, and different
        equivalence margins. Monte Carlo simulations enable accurate
        estimation of power and type I error rates, ensuring
        well-calibrated study designs. The statistical framework builds
        on established methods for equivalence testing and multiple
        hypothesis testing in bio-equivalence studies, as described in
        Schuirmann (1987) <doi:10.1007/BF01068419>, Mielke et al.
        (2018) <doi:10.1080/19466315.2017.1371071>, Shieh (2022)
        <doi:10.1371/journal.pone.0269128>, and Sozu et al. (2015)
        <doi:10.1007/978-3-319-22005-5>. Comprehensive documentation
        and vignettes guide users through implementation and
        interpretation of results.
License: Apache License (>= 2)
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
Imports: MASS, Rcpp (>= 1.0.13), data.table, matrixcalc, parallel
Suggests: ggplot2, kableExtra, knitr, rmarkdown, scales, testthat (>=
        3.0.0), tibble
VignetteBuilder: knitr
Config/testthat/edition: 3
LinkingTo: Rcpp, RcppArmadillo
URL: https://smartdata-analysis-and-statistics.github.io/SimTOST/,
        https://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: 2026-10-09 16:28:17 UTC; root
Author: Thomas Debray [aut, cre],
  Tim Friede [ctb],
  Johanna Munoz [ctb],
  Dewi Amaliah [ctb],
  Wei Wei [ctb],
  Marian Mitroiu [ctb],
  Scott McDonald [ctb],
  Biogen Inc [cph, fnd] (Copyright holder and funder of the original
    implementation v1.0.0),
  Smart Data Analysis and Statistics B.V. [cph, fnd] (Copyright holder
    and funder of subsequent developments)
Maintainer: Thomas Debray <tdebray@fromdatatowisdom.com>