Shows how the regions used by the decision criteria for two endpoints are numbered (Yamaguchi et al., submitted, Tables 1 and 2).
Usage
qdm_regions(prob = c("posterior", "predictive"), direction = "greater")Value
A character matrix of region labels (printed without quotes), with the conditions on endpoint 1 as column names and those on endpoint 2 as row names.
Details
For posterior probability, the target value (TV) and the minimum acceptable value (MAV) of each endpoint divide the plane of \((\theta_1, \theta_2)\) into nine regions. Columns correspond to endpoint 1 and rows to endpoint 2. For posterior predictive probability, the null values divide the plane into four regions.
For an endpoint on which smaller effects are favorable
(direction = "less" in the criterion), the inequalities of that
endpoint are reversed, so that region 1 is always the most favorable one:
for example, "theta2 < TV2", "TV2 <= theta2 < MAV2" and
"theta2 >= MAV2".
References
Yamaguchi, Y., Hu, H., Huang, Y., Han, C., Homma, G. and Wang, A. (submitted). Bivariate Bayesian quantitative decision-making for two endpoints in rare disease drug development.
Examples
qdm_regions("posterior")
#> theta1 > TV1 MAV1 < theta1 <= TV1 theta1 <= MAV1
#> theta2 > TV2 R1 R4 R7
#> MAV2 < theta2 <= TV2 R2 R5 R8
#> theta2 <= MAV2 R3 R6 R9
qdm_regions("predictive")
#> theta1 > NULL1 theta1 <= NULL1
#> theta2 > NULL2 R1 R3
#> theta2 <= NULL2 R2 R4
# Endpoint 2 is a safety endpoint (smaller effects are favorable)
qdm_regions("posterior", direction = c("greater", "less"))
#> theta1 > TV1 MAV1 < theta1 <= TV1 theta1 <= MAV1
#> theta2 < TV2 R1 R4 R7
#> TV2 <= theta2 < MAV2 R2 R5 R8
#> theta2 >= MAV2 R3 R6 R9
