Title Optimize the proportion of individuals in the design groups

Usage

optimize_groupsize(
  poped.db,
  props = c(poped.db$design$groupsize/sum(poped.db$design$groupsize)),
  trace = 1,
  ...
)

Arguments

poped.db: A PopED database.
props: The proportions of individuals in each group (relative to the total number of individuals) to start the optimization from.
trace: Should there be tracing of the optimization? Value can be integer values. Larger numbers give more information.
...: Arguments passed to ofv_fim and optim

Value

A list of the initial objective function value, optimal proportions, the objective function value with those proportions, the optimal number of individuals in each group (with integer number of individuals), and the objective function value with that number of individuals.

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_model_prediction(poped.db)


evaluate_design(poped.db)
#> $ofv
#> [1] 57.34239
#> 
#> $fim
#>                     CL         V        KA         d_CL        d_V        d_KA
#> CL         20100.83363 100.60318  57.19595    0.0000000     0.0000   0.0000000
#> V            100.60318  47.77375 -18.43690    0.0000000     0.0000   0.0000000
#> KA            57.19595 -18.43690  80.55401    0.0000000     0.0000   0.0000000
#> d_CL           0.00000   0.00000   0.00000 2019.7601648    73.4525   0.6961553
#> d_V            0.00000   0.00000   0.00000   73.4524996 48705.8108 117.2176533
#> d_KA           0.00000   0.00000   0.00000    0.6961553   117.2177  51.8762750
#> SIGMA[1,1]     0.00000   0.00000   0.00000 1428.9648684 31892.5113 417.9515723
#> SIGMA[2,2]     0.00000   0.00000   0.00000  274.2990996   640.9862  10.3145690
#>             SIGMA[1,1] SIGMA[2,2]
#> CL              0.0000    0.00000
#> V               0.0000    0.00000
#> KA              0.0000    0.00000
#> d_CL         1428.9649  274.29910
#> d_V         31892.5113  640.98623
#> d_KA          417.9516   10.31457
#> SIGMA[1,1] 353788.5599 6967.23365
#> SIGMA[2,2]   6967.2337  236.40519
#> 
#> $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 
#> 


# what are the optimal proportions of 
# individuals in the two groups in the study?
(n_opt <- optimize_groupsize(poped.db))
#> Initial proportions:
#>  0.5 0.5 
#> 
#> initial  value -57.342391 
#> final  value -57.747204 
#> converged
#> 
#> Optimized proportions:
#>  0.2984998 0.7015002 
#> 
#> Optimized number of individuals per group
#>  OFV:  57.74718 
#>  30 70 
#> 
#> $initial_props
#> [1] 0.5 0.5
#> 
#> $initial_ofv
#> [1] 57.34239
#> 
#> $opt_props
#> [1] 0.2984998 0.7015002
#> 
#> $opt_ofv_with_props
#> [1] 57.7472
#> 
#> $opt_n_per_group
#> [1] 30 70
#> 
#> $opt_ofv_with_n
#> [1] 57.74718
#> 

# How many individuals in the original design are needed to achieve an
# efficiency of 1 compared to the optimized design with n=100?
optimize_n_eff(poped.db,
               ofv_ref=n_opt$opt_ofv_with_n)
#> [1] 105.19