library(PopED)
############# START #################
## Create PopED database
## (warfarin model for optimization)
#####################################
## 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 samples).
## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
## -- parameter definition function
## -- names match parameters in function ff
sfg <- function(x,a,bpop,b,bocc){
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)
}
## -- Define initial design and design space
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
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(prop=0.01,add=0.25),
groupsize=32,
xt=c( 0.5,1,2,6,24,36,72,120),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
############# END ###################
## Create PopED database
## (warfarin model for optimization)
#####################################
##############
# D-family Optimization
##############
# below are a number of ways to optimize the problem
# ARS+BFGS+LS optimization of dose
# optimization with just a few iterations
# only to check that things are working
out_1 <- poped_optim(poped.db,opt_a =TRUE,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 1)
# cost function
# PRED at 120 hours
crit_fcn <- function(poped.db,...){
pred_df <- model_prediction(poped.db)
return(pred_df[pred_df$Time==120,"PRED"])
}
# maximize cost function
out_2 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 2)
# minimize the cost function
out_3 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 2,
maximize = FALSE,
evaluate_fim = FALSE)
if (FALSE) {
# RS+BFGS+LS optimization of sample times
# (longer run time than above but more likely to reach a maximum)
output <- poped_optim(poped.db,opt_xt=T,parallel = TRUE)
get_rse(output$FIM,output$poped.db)
plot_model_prediction(output$poped.db)
# optimization with only integer times allowed
poped.db.2 <- poped.db
poped.db.2$design_space$xt_space <- matrix(list(seq(1,120)),1,8)
output_2 <- poped_optim(poped.db.2,opt_xt=T,parallel = TRUE)
get_rse(output_2$FIM,output_2$poped.db)
plot_model_prediction(output_2$poped.db)
# Examine efficiency of sampling windows
plot_efficiency_of_windows(output_2$poped.db,xt_windows=0.5)
plot_efficiency_of_windows(output_2$poped.db,xt_windows=1)
# Adaptive Random Search (ARS, just a few samples here)
rs.output <- poped_optim(poped.db,opt_xt=T,method = "ARS",
control = list(ARS=list(iter=5)))
get_rse(rs.output$FIM,rs.output$poped.db)
# line search, DOSE and sample time optimization
ls.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "LS",
control = list(LS=list(line_length=5)))
# Adaptive random search,
# DOSE and sample time optimization
ars.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "ARS",
control = list(ARS=list(iter=5)))
# BFGS gradient search from the stats::optim() function,
# DOSE and sample time optimization
bfgs.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "BFGS",
control = list(BFGS=list(maxit=5)))
# genetic algorithm from the GA::ga() function,
# DOSE and sample time optimization
ga.output <- poped_optim(poped.db,opt_xt=T,opt_a=F,method = "GA",parallel=T)
# cost function with GA
# maximize
out_2 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
parallel = T,
method=c("GA"))
# cost function with GA
# minimize
out_2 <- poped_optim(poped.db,opt_a =TRUE,
ofv_fun=crit_fcn,
parallel = T,
method=c("GA"),
iter_max = 1,
maximize = F,
evaluate_fim = F)
# optimize distribution of individuals in 3 groups
poped_db_2 <- create.poped.database(
ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
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(prop=0.01,add=0.25),
groupsize=32,
m=3,
xt=list(c( 0.5,1,2,6,8),c(36,72,120),
c(10,12,14,16,18,20,22,24)),
minxt=0.01,
maxxt=120,
a=c(DOSE=70),
mina=c(DOSE=0.01),
maxa=c(DOSE=100))
opt_xt_inds <-
poped_optim(poped_db_2,
opt_a =TRUE,
opt_inds = TRUE,
control = list(ARS=list(iter=2),
BFGS=list(maxit=2),
LS=list(line_length=2)),
iter_max = 1)
##############
# E-family Optimization
##############
# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
bpop_vals,
ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",] <- c(0,1,0)
bpop_vals_ed_ln
## -- Define initial design and design space
poped.db <- create.poped.database(
ff_fun=ff.PK.1.comp.oral.sd.CL,
fg_fun=sfg,
fError_fun=feps.add.prop,
bpop=bpop_vals_ed_ln,
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)
# E_ln(D) optimization using Random search (just a few samples here)
output <- poped_optim(poped.db,opt_xt=TRUE,opt_a=TRUE,d_switch=0,
method = c("ARS","LS"),
control = list(ARS=list(iter=2),
LS=list(line_length=2)),
iter_max = 1)
get_rse(output$FIM,output$poped.db)
# ED with laplace approximation,
# optimization using Random search (just a few iterations here)
ars.output <- poped_optim(poped.db,opt_xt=T,opt_a=T,method = "ARS",
d_switch=0,use_laplace=TRUE,#laplace.fim=TRUE,
parallel=T,
control = list(ARS=list(iter=5)))
}
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