## Warfarin example from software comparison in:
## Nyberg et al., "Methods and software tools for design evaluation
## for population pharmacokinetics-pharmacodynamics studies",
## Br. J. Clin. Pharm., 2014.
## Optimization using an additive + proportional reidual error to
## avoid sample times at very low concentrations (time 0 or very late samoples).
library(PopED)
sfg <- function(x,a,bpop,b,bocc){
## -- parameter definition function
parameters=c(CL=bpop[1]*exp(b[1]),
V=bpop[2]*exp(b[2]),
KA=bpop[3]*exp(b[3]),
Favail=bpop[4],
DOSE=a[1])
return(parameters)
}
ff <- function(model_switch,xt,parameters,poped.db){
##-- Model: One comp first order absorption
with(as.list(parameters),{
y=xt
y=(DOSE*Favail*KA/(V*(KA-CL/V)))*(exp(-CL/V*xt)-exp(-KA*xt))
return(list(y=y,poped.db=poped.db))
})
}
feps <- function(model_switch,xt,parameters,epsi,poped.db){
## -- Residual Error function
## -- Proportional + additive
y <- ff(model_switch,xt,parameters,poped.db)[[1]]
y = y*(1+epsi[,1]) + epsi[,2]
return(list(y=y,poped.db=poped.db))
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_file="ff",
fg_file="sfg",
fError_file="feps",
bpop=c(CL=0.15, V=8, KA=1.0, Favail=1),
notfixed_bpop=c(1,1,1,0),
d=c(CL=0.07, V=0.02, KA=0.6),
sigma=c(0.01,0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0,
maxxt=120,
a=70,
mina=0,
maxa=100)
## create plot of model without variability
plot_model_prediction(poped.db)
## create plot of model with variability
plot_model_prediction(poped.db,PI=TRUE)
## evaluate initial design
evaluate_design(poped.db)
##############
# Optimization
##############
# below are a number of ways to optimize the problem
# Optimization of sample times
output <- poped_optim(poped.db, opt_xt = T, parallel = T)
get_rse(output$FIM,output$poped.db)
plot_model_prediction(output$poped.db)
# Examine efficiency of sampling windows
plot_efficiency_of_windows(output$poped.db,xt_windows=0.5)
# Optimization of DOSE and sampling times
output_D_T <- poped_optim(poped.db, opt_xt = T, opt_a = T, parallel = T)
summary(output_D_T)
plot_model_prediction(output_D_T$poped.db)
# Discrete optimization with only integer times allowed
# and Dose in units of 10
poped.db.discrete <- create.poped.database(poped.db,
xt=c( 1,2,3,6,24,36,72,120),
discrete_xt = list(0:120),
discrete_a = list(seq(10,100,10)))
output_discrete <- poped_optim(poped.db.discrete, opt_xt = T, opt_a = T, parallel = T)
get_rse(output_discrete$FIM,output_discrete$poped.db)
plot_model_prediction(output_discrete$poped.db)
# Optimization using a genetic algorithm
output_ga <- poped_optim(poped.db, opt_xt = T, parallel = T, method = c("GA"))
summary(output_ga)
plot_model_prediction(output_ga$poped.db)
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