In zhengyu888/NTIDSRSABE: An Reference-Scaled Average Bioequivalence Procedure For Therapeutic Index Drug

NTIDSRSABE

• Introduction
  • SW
    • Estimation of CV_wR (and CV_wT in full replicate designs)
    • SWT
    • SWR
    • SWRATE
  • BE
    • Four period (full) replicates
    • RSABE
    • ABE
  • ALLPK
    • Four period (full) replicates Data
    • ALLPK
• Examples
• Installation
• Session Information
• Contributors
• Disclaimer

.

An Reference-Scaled Average Bioequivalence Procedure For Therapeutic Index Drug

The library provides data sets (internal .rda and in CSV-format in /extdata/) supporting users in a black-box performance qualification (PQ) of their software installations. Users can analyze own data imported from CSV- and Excel-files. The methods given by the EMA for reference-scaling^1,2 are implemented.
Potential influence of outliers on the variability of the reference can be assessed by box plots of studentized and standardized residuals as suggested at a joint EGA/EMA workshop.³
Health Canada’s approach⁴ requiring a mixed-effects model is approximated by intra-subject contrasts.
Direct widening of the acceptance range as recommended by the Gulf Cooperation Council⁵ (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates) is provided as well.
In full replicate designs the variability of test and reference treatments can be assessed by s_wT/s_wR and the upper confidence limit of σ_wT/σ_wR (required for the WHO’s approach⁶ for reference-scaling of AUC).

TOC ↩

Methods

Estimation of CV_wR (and CV_wT in full replicate designs)

Called internally by functions SWT() and SWR()and SWRATE(). where all effects are fixed (i.e., ANOVA). Estimated by the function lm() of library stats.

SWT <- SWT(Data)
SWR <- SWR(Data)
SWRATE <- SWRATE(Data)
SWRATE2<-SWRATE2(SWT,DFCVT,SWR,DFCVR)

TOC ↩

NTIDSRSABE

Called by function NTIDSRSABE(). A linear model of ilat and effects
Sequence
where all effects are fixed (e.g., by an ANOVA). Estimated by the function lm() of library stats.

NTIDSRSABE<-NTIDSRSABE(Data)

TOC ↩

NTIDSABE

Called by function NTIDSABE(). A linear model of ilat and effects
Sequence
where all effects are fixed (e.g., by an ANOVA). Estimated by the function lm() of library stats.

NTIDSABE<-NTIDSABE(Data)

TOC ↩

ALLPK

Called by Data set ALLPK. A example of 4 PeriodS and 2 Sequences BE data can be found here: https://www.ema.europa.eu/en/documents/other/31-annex-ii-statistical-analysis-bioequivalence-study-example-data-set_en.pdf.

TOC ↩

Tested designs

Four period (full) replicates

TRTR | RTRT

• To use the development version, please install the released version from github first to get its dependencies right PowerTOST ≥1.5.3, dplyr, gmodels).

You need tools for building R packages from sources on your machine. For Windows users:

  - Download
    [Rtools](https://cran.r-project.org/bin/windows/Rtools/) from
    <span title="The Comprehensive R Archive Network">CRAN</span>
    and follow the suggestions of the installer.
  - Install `devtools` and build the development version by:

<!-- end list

install.packages("devtools", repos = "https://cloud.r-project.org/")
devtools::install_github("zhengyu888/NTIDSRSABE")

TOC ↩

Introduction

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

NTIDSRSABE

The goal of NTIDSRSABE is to ...

Installation

You can install the released version of NTIDSRSABE from github with:

devtools::install_github("zhengyu888/NTIDSRSABE")

Example

This is a basic example which shows you how to solve a common problem:

library(NTIDSRSABE)
## basic example code

What is special about using README.Rmd instead of just README.md? You can include R chunks like so:

summary(cars)

You'll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this. You could also use GitHub Actions to re-render README.Rmd every time you push.

You can also embed plots, for example:

plot(pressure)

In that case, don't forget to commit and push the resulting figure files, so they display on GitHub.

zhengyu888/NTIDSRSABE documentation built on Dec. 23, 2021, 9:19 p.m.