Showing 17 of total 17 results (show query)
detlew
PowerTOST:Power and Sample Size for (Bio)Equivalence Studies
Contains functions to calculate power and sample size for various study designs used in bioequivalence studies. Use known.designs() to see the designs supported. Power and sample size can be obtained based on different methods, amongst them prominently the TOST procedure (two one-sided t-tests). See README and NEWS for further information.
Maintained by Detlew Labes. Last updated 12 months ago.
13.7 match 20 stars 9.61 score 112 scripts 4 dependents
smartdata-analysis-and-statistics
SimTOST:Sample Size Estimation for Bio-Equivalence Trials Through Simulation
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.
Maintained by Thomas Debray. Last updated 27 days ago.
mcmcmulti-armmultiple-comparisonssample-size-calculationsample-size-estimationtrial-simulationopenblascpp
19.7 match 2 stars 6.47 score 7 scripts
helmut01
replicateBE:Average Bioequivalence with Expanding Limits (ABEL)
Performs comparative bioavailability calculations for Average Bioequivalence with Expanding Limits (ABEL). Implemented are 'Method A' / 'Method B' and the detection of outliers. If the design allows, assessment of the empiric Type I Error and iteratively adjusting alpha to control the consumer risk. Average Bioequivalence - optionally with a tighter (narrow therapeutic index drugs) or wider acceptance range (South Africa: Cmax) - is implemented as well.
Maintained by Helmut Schütz. Last updated 3 years ago.
18.9 match 9 stars 4.65 score 10 scripts
yboulag
cTOST:Finite Sample Correction of the Two One-Sided Tests in the Univariate Framework
A system containing easy-to-use tools to compute the bioequivalence assessment in the univariate framework using the methods proposed in Boulaguiem et al. (2023) <doi:10.1101/2023.03.11.532179>.
Maintained by Younes Boulaguiem. Last updated 1 months ago.
bioequivalenceequivalencehighly-variable-drugsstatistics
16.4 match 4.48 score 4 scripts
cran
BE:Bioequivalence Study Data Analysis
Analyze bioequivalence study data with industrial strength. Sample size could be determined for various crossover designs, such as 2x2 design, 2x4 design, 4x4 design, Balaam design, Two-sequence dual design, and William design. Reference: Chow SC, Liu JP. Design and Analysis of Bioavailability and Bioequivalence Studies. 3rd ed. (2009, ISBN:978-1-58488-668-6).
Maintained by Kyun-Seop Bae. Last updated 2 years ago.
14.8 match 1 stars 2.63 score 43 scripts
wuqian77
TrialSize:R Functions for Chapter 3,4,6,7,9,10,11,12,14,15 of Sample Size Calculation in Clinical Research
Functions and Examples in Sample Size Calculation in Clinical Research.
Maintained by Vicky Qian Wu. Last updated 4 months ago.
9.0 match 3 stars 3.78 score 95 scripts 1 dependents
johnjsl7
daewr:Design and Analysis of Experiments with R
Contains Data frames and functions used in the book "Design and Analysis of Experiments with R", Lawson(2015) ISBN-13:978-1-4398-6813-3.
Maintained by John Lawson. Last updated 2 years ago.
7.3 match 3 stars 3.83 score 217 scripts 3 dependents
certara
mbbe:Model Based Bio-Equivalence
Uses several Nonlinear Mixed effect (NONMEM) models (as NONMEM control files) to perform bootstrap model averaging and Monte Carlo Simulation for Model Based Bio-Equivalence (MBBE). Power is returned as the fraction of the simulations with successful bioequivalence (BE) test, for maximum concentration (Cmax), Area under the curve to the last observed value (AUClast) and Area under the curve extrapolate to infinity (AUCinf). See Hooker, A. (2020) Improved bioequivalence assessment through model-informed and model-based strategies <https://www.fda.gov/media/138035/download>.
Maintained by Mark Sale. Last updated 1 years ago.
2.8 match 4.30 score 3 scriptsdetlew
Power2Stage:Power and Sample-Size Distribution of 2-Stage Bioequivalence Studies
Contains functions to obtain the operational characteristics of bioequivalence studies in Two-Stage Designs (TSD) via simulations.
Maintained by Detlew Labes. Last updated 1 years ago.
3.4 match 1 stars 3.11 score 13 scripts
philippallmann
jocre:Joint Confidence Regions
Computing and plotting joint confidence regions and intervals. Regions include classical ellipsoids, minimum-volume or minimum-length regions, and an empirical Bayes region. Intervals include the TOST procedure with ordinary or expanded intervals and a fixed-sequence procedure. Such regions and intervals are useful e.g., for the assessment of multi-parameter (bio-)equivalence. Joint confidence regions for the mean and variance of a normal distribution are available as well.
Maintained by Philip Pallmann. Last updated 8 years ago.
3.8 match 1 stars 2.00 score 9 scripts
tjaki
PK:Basic Non-Compartmental Pharmacokinetics
Estimation of pharmacokinetic parameters using non-compartmental theory.
Maintained by Thomas Jaki. Last updated 2 years ago.
2.3 match 2.59 score 13 scripts 1 dependentscran
sasLM:'SAS' Linear Model
This is a core implementation of 'SAS' procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS.' Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).
Maintained by Kyun-Seop Bae. Last updated 6 months ago.
1.9 match 2.55 score 3 dependentscran
EQUIVNONINF:Testing for Equivalence and Noninferiority
Making available in R the complete set of programs accompanying S. Wellek's (2010) monograph ''Testing Statistical Hypotheses of Equivalence and Noninferiority. Second Edition'' (Chapman&Hall/CRC).
Maintained by Stefan Wellek. Last updated 4 years ago.
3.3 match 1.30 score
detlew
randomizeBE:Create a Random List for Crossover Studies
Contains a function to randomize subjects, patients in groups of sequences (treatment sequences). If a blocksize is given, the randomization will be done within blocks. The randomization may be controlled by a Wald-Wolfowitz runs test. Functions to obtain the p-value of that test are included. The package is mainly intended for randomization of bioequivalence studies but may be used also for other clinical crossover studies. Contains two helper functions sequences() and williams() to get the sequences of commonly used designs in BE studies.
Maintained by D. Labes. Last updated 2 years ago.
0.5 match 1 stars 1.30 score 6 scripts