| Title: | Calculate Regional Consistency Probabilities for Multi-Regional Clinical Trials |
| Version: | 1.0.0 |
| Description: | Provides methods to calculate approximate regional consistency probabilities using Method 1 and Method 2 proposed by the Japanese Ministry of Health, Labor and Welfare (2007) https://www.pmda.go.jp/files/000153265.pdf. These methods are useful for assessing regional consistency in multi-regional clinical trials. The package can calculate unconditional, joint, and conditional regional consistency probabilities. For technical details, please see Homma (2024) <doi:10.1002/pst.2358>. |
| License: | MIT + file LICENSE |
| Imports: | mvtnorm, stats |
| Suggests: | testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-05-13 12:27:59 UTC; i_lik |
| Author: | Gosuke Homma [aut, cre] |
| Maintainer: | Gosuke Homma <my.name.is.gosuke@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-05-15 14:00:06 UTC |
Calculate Regional Consistency Probabilities
Description
This function calculates approximate regional consistency probabilities using Methods 1 and 2 proposed by Japanese MHLW (2007). The function can obtain:
Unconditional regional consistency probabilities
Joint regional consistency probabilities
Conditional regional consistency probabilities
For technical details, please see Homma (2024)
Usage
regional.consistency.probs(f.s, PI, alpha, power, seed)
Arguments
f.s |
A numeric vector representing the proportion of patients in region s(=1,...,S) among patients in the entire trial population. Values must sum to 1. |
PI |
A numeric value specifying the threshold for Method 1 (typically set at 0.5). |
alpha |
A numeric value representing the one-sided level of significance. |
power |
A numeric value representing the target power. |
seed |
A random number seed. |
Value
A list containing the following components:
- f.s
The input proportion of patients in each region
- PI
The input threshold value for Method 1
- alpha
The input one-sided significance level
- power
The input target power
- seed
The input seed number
- Uncond.Method1
Unconditional regional consistency probability for Method 1
- Joint.Method1
Joint regional consistency probability for Method 1
- Cond.Method1
Conditional regional consistency probability for Method 1
- Uncond.Method2
Unconditional regional consistency probability for Method 2
- Joint.Method2
Joint regional consistency probability for Method 2
- Cond.Method2
Conditional regional consistency probability for Method 2
Examples
regional.consistency.probs(
f.s = c(0.1, 0.45, 0.45),
PI = 0.5,
alpha = 0.025,
power = 0.8,
seed = 123
)