SimTOST/DESCRIPTION at main · smartdata-analysis-and-statistics/SimTOST · GitHub

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Package: SimTOST
Title: Sample Size Estimation for Bio-Equivalence Trials Through Simulation
Version: 1.0.2
Authors@R: c(
    person(given = "Thomas",
           family = "Debray",
           email = "tdebray@fromdatatowisdom.com",
           role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-1790-2719")),
    person(given = "Johanna",
           family = "Munoz",
           email = "johanna.munoz@fromdatatowisdom.com",
           role = c("aut")),
    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"))
    )
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)
RoxygenNote: 7.3.2
Imports:
    MASS,
    Rcpp (>= 1.0.13),
    data.table,
    matrixcalc,
    parallel
Suggests:
    PowerTOST,
    ggplot2,
    kableExtra,
    knitr,
    rmarkdown,
    testthat (>= 3.0.0),
    tibble,
    tinytest
VignetteBuilder: knitr
Config/testthat/edition: 3
LinkingTo: Rcpp, RcppArmadillo
URL: https://smartdata-analysis-and-statistics.github.io/SimTOST/