prms {adaptIVPT}

Compute the passing rate for the mixed scaling approach in bioequivalence (BE) studies

Description

This function runs Monte Carlo simulations to compute the passing rate (PR) of the mixed scaling (MS) approach.

Usage

prms(n, r, params = list(), nsim = 1000, ncores = NULL)

Arguments

n

The number of donors in each simulation.

r

The number of replicates from each donor for each simulated dataset.

params

(Optional) The list of true parameters to be assumed in data generation.

• sigma_W0 - A regulatory constant set by the FDA. Defaults to 0.25.

• sigma_WT - The true standard deviation of the test formulation population.

• sigma_WR - The true standard deviation of the reference formulation population.

• GMR - The geometric mean ratio of the test and reference values of the pharmacokinetic measures (e.g., Jmax or AUC). If the test-formulation measure is greater than that of the reference formulation, then GMR is typically set to 1.05, which is the initial value of this function. If the reference-formulation measure is bigger, then GMR is typically 0.95. Defaults to 0.95.

• m - Another regulatory constant that determines the bounds within which the estimated GMR should fall for bioequivalence to be established. Defaults to 1.25, representing 80-125% average BE limits, which is the FDA recommendation.

• sig_level - The significance level (alpha-level). Defaults to 0.05.

nsim

(Optional) The number of total simulations to be conducted. Defaults to 1,000.

ncores

(Optional) The number of CPU cores to use for parallel processing (OpenMP). If R hasn't been installed with OpenMP configured, this will not take effect. When OpenMP is available, it should not exceed the number of existing cores. If unspecified, it will default to 2 cores or the number of existing cores, whichever is smaller.

Value

A list of lists

• parameters - A list of true parameter settings.

• passing_rate - The estimated passing rate.

• runtime - The total elapsed time charged for the execution of the program.

Author(s)

Daeyoung Lim, [e-mail]

References

Davit, B. M., Chen, M. L., Conner, D. P., Haidar, S. H., Kim, S., Lee, C. H., Lionberger, R. A., Makhlouf, F. T., Nwakama, P. E., Patel, D. T., Schuirmann, D. J., & Yu, L. X. (2012). Implementation of a reference-scaled average bioequivalence approach for highly variable generic drug products by the US Food and Drug Administration. The AAPS journal, 14(4), 915-924.

Examples

out <- prms(10, 6, nsim = 2)