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
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
NeedsCompilation: yes
Packaged: 2026-10-09 14:13:11 UTC; johanna
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>
Repository: CRAN
Date/Publication: 2026-10-09 15:20:07 UTC
