# NOT RUN {
## Setting up dummy sampling for fast execution of example
## Please use 4 chains and 100x more warmup & iter in practice
.user_mc_options <- options(OncoBayes2.MC.warmup=10, OncoBayes2.MC.iter=20, OncoBayes2.MC.chains=1)
library(RBesT)
## Example from Neuenschwander, B., et al. (2009). Stats in Medicine
num_comp <- 1 # one investigational drug
num_inter <- 0 # no drug-drug interactions need to be modeled
num_groups <- nlevels(hist_SA$group_id) # no stratification needed
num_strata <- 1 # no stratification needed
dref <- 50
## Since there is no prior information the hierarchical model
## is not used in this example by setting tau to (almost) 0.
blrmfit <- blrm_exnex(
cbind(num_toxicities, num_patients - num_toxicities) ~
1 + log(drug_A / dref) |
0 |
group_id,
data = hist_SA,
prior_EX_mu_mean_comp = matrix(
c(logit(1/2), # mean of intercept on logit scale
log(1)), # mean of log-slope on logit scale
nrow = num_comp,
ncol = 2
),
prior_EX_mu_sd_comp = matrix(
c(2, # sd of intercept
1), # sd of log-slope
nrow = num_comp,
ncol = 2
),
## Here we take tau as known and as zero.
## This disables the hierarchical prior which is
## not required in this example as we analyze a
## single trial.
prior_EX_tau_mean_comp = matrix(
c(0, 0),
nrow = num_comp,
ncol = 2
),
prior_EX_tau_sd_comp = matrix(
c(1, 1),
nrow = num_comp,
ncol = 2
),
prior_EX_prob_comp = matrix(1, nrow = num_comp, ncol = 1),
prior_tau_dist = 0,
prior_PD = FALSE
)
## Recover user set sampling defaults
options(.user_mc_options)
# }
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