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The goal of baskoptr is to supply a unified framework for optimizing basket trial designs. To this end, the package supplies several utility functions and also a function for executing optimization algorithms on basket trial designs.
You can install the development version of baskoptr from GitHub with:
# install.packages("pak")
pak::pak("LukasDSauer/baskoptr")In the following example, we optimize Fujikawa et al.’s basket trial design with respect to the experiment-wise power utility function using the simulated annealing algorithm.
library(baskoptr)
# Optimizing a three-basket trial design using Fujikawa's beta-binomial
# sharing approach
design <- baskwrap::setup_fujikawa_x(k = 3, shape1 = 1, shape2 = 1,
p0 = 0.2, backend = "exact")
detail_params <- list(p1 = c(0.5, 0.2, 0.2),
n = 20,
weight_fun = baskwrap::weights_jsd,
logbase = exp(1),
verbose = FALSE)
utility_params <- list(penalty = 1, thresh = 0.1)
opt_design_gen(design = design,
utility = u_ewp,
algorithm = optimizr::simann,
detail_params = detail_params,
utility_params = utility_params,
algorithm_params = list(par = c(lambda = 0.99,
epsilon = 2,
tau = 0.5),
lower = c(lambda = 0.001,
epsilon = 1,
tau = 0.001),
upper = c(lambda = 0.999,
epsilon = 10,
tau = 0.999),
control = list(maxit = 10,
temp = 10,
fnscale = -1,
REPORT = -1)))
#> $par
#> lambda epsilon tau
#> 0.99 2.00 0.50
#>
#> $value
#> [1] 0.8036443These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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