# 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)
dref <- c(300, 960)
num_comp <- 2 # two investigational drugs
num_inter <- 1 # one drug-drug interaction needs to be modeled
num_groups <- nlevels(codata_combo2$group_id) # no stratification needed
num_strata <- 1 # no stratification needed
blrmfit <- blrm_exnex(
cbind(num_toxicities, num_patients - num_toxicities) ~
1 + I(log(drug_A / dref[1])) |
1 + I(log(drug_B / dref[2])) |
0 + I(drug_A/dref[1] *drug_B/dref[2]) |
group_id,
data = codata_combo2,
prior_EX_mu_mean_comp = matrix(
c(logit(0.1), 0, # hyper-mean of (intercept, log-slope) for drug A
logit(0.1), 0), # hyper-mean of (intercept, log-slope) for drug B
nrow = num_comp,
ncol = 2,
byrow = TRUE
),
prior_EX_mu_sd_comp = matrix(
c(3.33, 1, # hyper-sd of mean mu for (intercept, log-slope) for drug A
3.33, 1), # hyper-sd of mean mu for (intercept, log-slope) for drug B
nrow = num_comp,
ncol = 2,
byrow = TRUE
),
prior_EX_tau_mean_comp = matrix(
c(log(0.25), log(0.125),
log(0.25), log(0.125)),
nrow = num_comp,
ncol = 2,
byrow = TRUE
),
prior_EX_tau_sd_comp = matrix(
c(log(4) / 1.96, log(4) / 1.96,
log(4) / 1.96, log(4) / 1.96),
nrow = num_comp,
ncol = 2,
byrow = TRUE
),
prior_EX_mu_mean_inter = 0,
prior_EX_mu_sd_inter = 1.121,
prior_EX_tau_mean_inter = matrix(log(0.125), nrow = num_inter, ncol = num_strata),
prior_EX_tau_sd_inter = matrix(log(4) / 1.96, nrow = num_inter, ncol = num_strata),
prior_is_EXNEX_comp = rep(FALSE, num_comp),
prior_is_EXNEX_inter = rep(FALSE, num_inter),
prior_EX_prob_comp = matrix(1, nrow = num_groups, ncol = num_comp),
prior_EX_prob_inter = matrix(1, nrow = num_groups, ncol = num_inter),
prior_tau_dist = 1
)
## Recover user set sampling defaults
options(.user_mc_options)
# }
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