## using libary models and reparameterizing the problen to KA, KE and V
## optimization of dose and dose interval
library(PopED)
## -- names match parameters in function defined in ff_file
fg.PK.1.comp.oral.md.param.2 <- function(x,a,bpop,b,bocc){
## -- parameter definition function
parameters=c( V=bpop[1]*exp(b[1]),
KA=bpop[2]*exp(b[2]),
KE=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1],
TAU=a[2])
return( parameters )
}
## -- Define design and design space
poped.db <- create.poped.database(ff_file="ff.PK.1.comp.oral.md.KE",
fg_file="fg.PK.1.comp.oral.md.param.2",
fError_file="feps.add.prop",
groupsize=20,
m=2,
sigma=c(0.04,5e-6),
bpop=c(V=72.8,KA=0.25,KE=3.75/72.8,Favail=0.9),
d=c(V=0.09,KA=0.09,KE=0.25^2),
notfixed_bpop=c(1,1,1,0),
notfixed_sigma=c(0,0),
xt=c( 1,2,8,240,245),
minxt=c(0,0,0,240,240),
maxxt=c(10,10,10,248,248),
bUseGrouped_xt=1,
a=list(c(DOSE=20,TAU=24),c(DOSE=40, TAU=24)),
maxa=c(DOSE=200,TAU=40),
mina=c(DOSE=0,TAU=2))
## create plot of model without variability
plot_model_prediction(poped.db)
## create plot of model with variability
plot_model_prediction(poped.db,IPRED=T,DV=T,separate.groups=T)
## evaluate initial design
evaluate_design(poped.db)
shrinkage(poped.db)
# Optimization of sample times
output <- poped_optim(poped.db, opt_xt =TRUE, parallel=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, parallel = TRUE)
summary(output_2)
get_rse(output_2$FIM,output_2$poped.db)
plot_model_prediction(output_2$poped.db)
# Optimization of sample times with only integer time points in design space
# faster than continuous optimization in this case
poped.db.discrete <- create.poped.database(poped.db,discrete_xt = list(0:248))
output_discrete <- poped_optim(poped.db.discrete, opt_xt=T, parallel = TRUE)
summary(output_discrete)
get_rse(output_discrete$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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