rss {adaptIVPT} | R Documentation |
Reestimate the sample size for the adaptive design in bioequivalence (BE) studies using mixed criterion.
Description
This function reestimates the sample size using mixed criterion required for target power, using binary search. The power (passing rate) function of mixed criterion testing lacks a closed-form expression. Thus, sample size (re-)estimation requires a binary search, after identifying an n
where the passing rate exceeds the desired level.
Usage
rss(n, r, S_WR, 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. |
S_WR |
The estimated standard deviation of the reference measurements. The reference-scaled average bioequivalence approach is used if S_WR > 0.249 and the average bioequivalence approach otherwise. |
params |
(Optional) The list of true parameters to be assumed in data generation.
|
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. -
rss
- The reestimated sample size. -
runtime
- The total elapsed time charged for the execution of the program.
Author(s)
Daeyoung Lim, daeyoung.lim@uconn.edu
References
Potvin, D., DiLiberti, C. E., Hauck, W. W., Parr, A. F., Schuirmann, D. J., & Smith, R. A. (2008). Sequential design approaches for bioequivalence studies with crossover designs. Pharmaceutical Statistics: The Journal of Applied Statistics in the Pharmaceutical Industry, 7(4), 245-262.
Examples
out <- rss(10, 6, S_WR = 0.22, nsim = 2)