Optimization function for D-family, E-family and Laplace approximated ED designs

Optimize the objective function for D-family, E-family and Laplace approximated ED designs. Right now there is only one optimization algorithm used in this function

Adaptive random search. See RS_opt.

This function takes information from the PopED database supplied as an argument. The PopED database supplies information about the the model, parameters, design and methods to use. Some of the arguments coming from the PopED database can be overwritten; if they are supplied then they are used instead of the arguments from the PopED database.

Usage

LEDoptim(
  poped.db,
  model_switch = NULL,
  ni = NULL,
  xt = NULL,
  x = NULL,
  a = NULL,
  bpopdescr = NULL,
  ddescr = NULL,
  maxxt = NULL,
  minxt = NULL,
  maxa = NULL,
  mina = NULL,
  ofv_init = 0,
  fim_init = 0,
  trflag = TRUE,
  header_flag = TRUE,
  footer_flag = TRUE,
  opt_xt = poped.db$settings$optsw[2],
  opt_a = poped.db$settings$optsw[4],
  opt_x = poped.db$settings$optsw[3],
  out_file = NULL,
  d_switch = FALSE,
  use_laplace = T,
  laplace.fim = FALSE,
  use_RS = poped.db$settings$bUseRandomSearch,
  ...
)

Arguments

poped.db

A PopED database.

model_switch

A matrix that is the same size as xt, specifying which model each sample belongs to.

ni

A vector of the number of samples in each group.

xt

A matrix of sample times. Each row is a vector of sample times for a group.

x

A matrix for the discrete design variables. Each row is a group.

a

A matrix of covariates. Each row is a group.

bpopdescr

Matrix defining the fixed effects, per row (row number = parameter_number) we should have:

column 1 the type of the distribution for E-family designs (0 = Fixed, 1 = Normal, 2 = Uniform, 3 = User Defined Distribution, 4 = lognormal and 5 = truncated normal)
column 2 defines the mean.
column 3 defines the variance of the distribution (or length of uniform distribution).

ddescr

Matrix defining the diagonals of the IIV (same logic as for the bpopdescr).

maxxt

Matrix or single value defining the maximum value for each xt sample. If a single value is supplied then all xt values are given the same maximum value.

minxt

Matrix or single value defining the minimum value for each xt sample. If a single value is supplied then all xt values are given the same minimum value

maxa

Vector defining the max value for each covariate. If a single value is supplied then all a values are given the same max value

mina

Vector defining the min value for each covariate. If a single value is supplied then all a values are given the same max value

ofv_init

The initial OFV. If set to zero then it is computed.

fim_init

The initial value of the FIM. If set to zero then it is computed.

trflag

Should the optimization be output to the screen and to a file?

header_flag

Should the header text be printed out?

footer_flag

Should the footer text be printed out?

opt_xt

Should the sample times be optimized?

opt_a

Should the continuous design variables be optimized?

opt_x

Should the discrete design variables be optimized?

out_file

Which file should the output be directed to? A string, a file handle using file or "" will output to the screen.

d_switch

******START OF CRITERION SPECIFICATION OPTIONS**********

D-family design (1) or ED-family design (0) (with or without parameter uncertainty)

use_laplace

Should the Laplace method be used in calculating the expectation of the OFV?

laplace.fim

Should an E(FIM) be calculated when computing the Laplace approximated E(OFV). Typically the FIM does not need to be computed and, if desired, this calculation is done using the standard MC integration technique, so can be slow.

use_RS

should the function use a random search algorithm?

...

arguments passed to evaluate.fim and ofv_fim.

Examples

library(PopED)

############# START #################
## Create PopED database
## (warfarin model for optimization
##  with parameter uncertainty)
#####################################

## 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).

## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.CL
#> function (model_switch, xt, parameters, poped.db) 
#> {
#>     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))
#>     })
#> }
#> <bytecode: 0x557079188b38>
#> <environment: namespace:PopED>

## -- 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) 
}

# 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
#>          bpop_vals         
#> CL     4      0.15 0.000225
#> V      4      8.00 0.640000
#> KA     4      1.00 0.010000
#> Favail 0      1.00 0.000000

## -- 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)

############# END ###################
## Create PopED database
## (warfarin model for optimization
##  with parameter uncertainty)
#####################################

# warfarin ed model

if (FALSE) { # \dontrun{
  
  LEDoptim(poped.db) 
  
  LEDoptim(poped.db,opt_xt=T,rsit=10) 
  
  LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE) 

  LEDoptim(poped.db,opt_xt=T,rsit=10,laplace.fim=TRUE) 
  
  LEDoptim(poped.db,opt_xt=T,rsit=10,use_laplace=FALSE) 
  
  ## testing header and footer
  LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
           out_file="foobar.txt") 
  
  ff <- LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
                 trflag=FALSE) 
  
  LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
           header_flag=FALSE) 
  
  LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
           out_file="") 
  
  LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
           footer_flag=FALSE) 
  
  LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
           footer_flag=FALSE, header_flag=FALSE) 
  
  LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
           footer_flag=FALSE, header_flag=FALSE,out_file="foobar.txt") 
  
  LEDoptim(poped.db,opt_xt=T,rsit=10,d_switch=TRUE,
           footer_flag=FALSE, header_flag=FALSE,out_file="") 

} # }

Optimization function for D-family, E-family and Laplace approximated ED designs

Usage

Arguments

See also

Examples