Optimize the number of subjects based on desired uncertainty of a parameter.

Optimize the number of subjects, based on the current design and the desired uncertainty of a single parameter

Usage

optimize_n_rse(
  poped.db,
  bpop_idx,
  need_rse,
  use_percent = TRUE,
  allowed_values = seq(poped.db$design$m, sum(poped.db$design$groupsize) * 5, by =
    poped.db$design$m)
)

Arguments

poped.db

A PopED database.

bpop_idx

The index number of the parameter, currently only bpop parameters are allowed.

need_rse

The relative standard error (RSE) one would like to achieve (in percent, by default).

use_percent

Should the RSE be represented as a percentage (T/F)?

allowed_values

A vector of the allowed total number of subjects in the study.

Value

The total number of subjects needed and the RSE of the parameter.

Examples

# 2 design groups with either early or late samples
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
                                  fg_fun=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) 
                                  },
                                  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(0.01,0.25),
                                  xt=list(c(1,2,3),c(4,5,20,120)),
                                  groupsize=50,
                                  minxt=0.01,
                                  maxxt=120,
                                  a=70,
                                  mina=0.01,
                                  maxa=100)

# plot of the design
plot_model_prediction(poped.db)


# the current RSE values
evaluate_design(poped.db)$rse
#>         CL          V         KA       d_CL        d_V       d_KA SIGMA[1,1] 
#>   4.742175   1.908239  11.706099  38.149149  23.388391  23.297863  28.859652 
#> SIGMA[2,2] 
#>  48.150485 

# number of individuals if CL should have 10% RSE
optimize_n_rse(poped.db,
                bpop_idx=1, # for CL
                need_rse=10) # the RSE you want
#> $n
#> [1] 24
#> 
#> $par_rse
#>       CL 
#> 9.679923 
#>