## using libary models and reparameterizing the problen to KA, KE and V
## optimization of dose and dose interval
library(babelmixr2)
library(PopED)
f <- function() {
ini({
tV <- 72.8
tKa <- 0.25
tKe <- 3.75/72.8
tFavail <- fix(0.9)
eta.v ~ 0.09
eta.ka ~ 0.09
eta.ke ~ 0.25^2
prop.sd <- fix(sqrt(0.04))
add.sd <- fix(sqrt(5e-6))
})
model({
V <- tV*exp(eta.v)
KA <- tKa*exp(eta.ka)
KE <- tKe*exp(eta.ke)
Favail <- tFavail
N <- floor(time/TAU)+1
y <- (DOSE*Favail/V)*(KA/(KA - KE)) *
(exp(-KE * (time - (N - 1) * TAU)) * (1 - exp(-N * KE * TAU))/(1 - exp(-KE * TAU)) -
exp(-KA * (time - (N - 1) * TAU)) * (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU)))
y ~ prop(prop.sd) + add(add.sd)
})
}
# minxt, maxxt
e <- et(list(c(0, 10),
c(0, 10),
c(0, 10),
c(240, 248),
c(240, 248))) %>%
as.data.frame()
#xt
e$time <- c(1,2,8,240,245)
babel.db <- nlmixr2(f, e, "poped",
popedControl(groupsize=20,
bUseGrouped_xt=TRUE,
a=list(c(DOSE=20,TAU=24),
c(DOSE=40, TAU=24)),
maxa=c(DOSE=200,TAU=24),
mina=c(DOSE=0,TAU=24)))
## create plot of model without variability
plot_model_prediction(babel.db)
## create plot of model with variability
plot_model_prediction(babel.db,IPRED=T,DV=T,separate.groups=T)
## evaluate initial design
evaluate_design(babel.db)
shrinkage(babel.db)
# Optimization of sample times
output <- poped_optim(babel.db, opt_xt =TRUE)
# Evaluate optimization results
summary(output)
get_rse(output$FIM,output$poped.db)
plot_model_prediction(output$poped.db)
# Optimization of sample times, doses and dose intervals
output_2 <- poped_optim(output$poped.db, opt_xt =TRUE, opt_a = TRUE)
summary(output_2)
get_rse(output_2$FIM,output_2$poped.db)
plot_model_prediction(output_2$poped.db)
# Optimization of sample times with only integer time points in design space
# faster than continuous optimization in this case
babel.db.discrete <- create.poped.database(babel.db,discrete_xt = list(0:248))
output_discrete <- poped_optim(babel.db.discrete, opt_xt=T)
summary(output_discrete)
get_rse(output_discrete$FIM,output_discrete$poped.db)
plot_model_prediction(output_discrete$poped.db)
# Efficiency of sampling windows
plot_efficiency_of_windows(output_discrete$poped.db, xt_windows=1)
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