power_cal

Internal function to calculate the power across all comparators

internal

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.

Thomas Debray

SimTOST

Sample Size Estimation for Bio-Equivalence Trials Through
Simulation

Johanna Munoz

Dewi Amaliah

Wei Wei

Marian Mitroiu

Scott McDonald

Biogen Inc 

power_cal function

<dl><dt>n</dt>
<dd>sample size</dd>
<dt>nsim</dt>
<dd>number of simulated studies</dd>
<dt>param</dt>
<dd>list of parameters (mean,sd,tar)</dd>
<dt>param.d</dt>
<dd>design parameters</dd>
<dt>seed</dt>
<dd>main seed</dd>
<dt>ncores</dt>
<dd>number of cores</dd></dl>

Arguments

Calculate the power across all comparators — power_cal

<dl>

<dt>n</dt>
<dd>sample size</dd>


<dt>nsim</dt>
<dd>number of simulated studies</dd>


<dt>param</dt>
<dd>list of parameters (mean,sd,tar)</dd>


<dt>param.d</dt>
<dd>design parameters</dd>


<dt>seed</dt>
<dd>main seed</dd>


<dt>ncores</dt>
<dd>number of cores</dd>

</dl>

power_cal: Calculate the power across all comparators

Description

Usage

Value

Arguments