Predict shrinkage of empirical Bayes estimates (EBEs) in a population model

Usage

shrinkage(poped.db, use_mc = FALSE, num_sim_ids = 1000, use_purrr = FALSE)

Arguments

poped.db: A PopED database
use_mc: Should the calculation be based on monte-carlo simulations. If not then then a first order approximation is used
num_sim_ids: If use_mc=TRUE, how many individuals should be simulated to make the computations.
use_purrr: If use_mc=TRUE then should the method use the package purrr in calculations? This may speed up computations (potentially).

Value

The shrinkage computed in variance units, standard deviation units and the relative standard errors of the EBEs.

References

Combes, F. P., Retout, S., Frey, N., & Mentre, F. (2013). Prediction of shrinkage of individual parameters using the Bayesian information matrix in non-linear mixed effect models with evaluation in pharmacokinetics. Pharmaceutical Research, 30(9), 2355-67. doi:10.1007/s11095-013-1079-3 .
Hennig, S., Nyberg, J., Fanta, S., Backman, J. T., Hoppu, K., Hooker, A. C., & Karlsson, M. O. (2012). Application of the optimal design approach to improve a pretransplant drug dose finding design for ciclosporin. Journal of Clinical Pharmacology, 52(3), 347-360. doi:10.1177/0091270010397731 .

Examples

library(PopED)

############# START #################
## Create PopED database
## (warfarin example)
#####################################

## 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. 

## 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) 
}

## -- Define model, parameters, initial design
poped.db <- create.poped.database(ff_fun=ff.PK.1.comp.oral.sd.CL,
                                  fg_fun=sfg,
                                  fError_fun=feps.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),
                                  groupsize=32,
                                  xt=c( 0.5,1,2,6,24,36,72,120),
                                  a=c(DOSE=70))

############# END ###################
## Create PopED database
## (warfarin example)
#####################################

shrinkage(poped.db)
#> # A tibble: 3 × 5
#>     d_CL    d_V   d_KA type       group
#>    <dbl>  <dbl>  <dbl> <chr>      <chr>
#> 1 0.0244 0.174  0.0301 shrink_var grp_1
#> 2 0.0123 0.0910 0.0152 shrink_sd  grp_1
#> 3 0.0413 0.0590 0.134  se         grp_1