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.
Version: 1.0.2 Imports: MASS, Rcpp (≥ 1.0.13), data.table, matrixcalc, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: PowerTOST, ggplot2, kableExtra, knitr, rmarkdown, testthat (≥ 3.0.0), tibble, tinytest Published: 2025-02-18 DOI: 10.32614/CRAN.package.SimTOST Author: Thomas Debray [aut, cre], Johanna Munoz [aut], Dewi Amaliah [ctb], Wei Wei [ctb], Marian Mitroiu [ctb], Scott McDonald [ctb], Biogen Inc [cph, fnd] Maintainer: Thomas Debray <tdebray at fromdatatowisdom.com> License: Apache License (≥ 2) URL: https://smartdata-analysis-and-statistics.github.io/SimTOST/ NeedsCompilation: yes Materials: README NEWS CRAN checks: SimTOST results Documentation: Reference manual: SimTOST.pdf Vignettes: Introduction (source, R code)Please use the canonical form https://CRAN.R-project.org/package=SimTOST to link to this page.
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