PopED (version 0.7.0)

optimize_groupsize: Title Optimize the proportion of individuals in the design groups

Description

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,
  ...
)

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.

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

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)


# what are the optimal proportions of 
# individuals in the two groups in the study?
(n_opt <- optimize_groupsize(poped.db))

# 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)