PopED (version 0.3.2)

get_rse: Compute the expected parameter relative standard errors

Description

This function computes the expected relative standard errors of a model given a design and a previously computed FIM.

Usage

get_rse(fmf, poped.db, bpop = poped.db$parameters$bpop[, 2, drop = F],
  d = poped.db$parameters$d[, 2, drop = F], docc = poped.db$parameters$docc,
  sigma = poped.db$parameters$sigma, use_percent = T,
  fim.calc.type = poped.db$settings$iFIMCalculationType)

Arguments

fmf

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

poped.db

A PopED database.

bpop

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).

Can also just supply the parameter values as a vector c() if no uncertainty around the parameter value is to be used.

d

Matrix defining the diagnonals of the IIV (same logic as for the fixed efects matrix bpop to define uncertainty). One can also just supply the parameter values as a c().

docc

Matrix defining the IOV, the IOV variances and the IOV distribution as for d and bpop.

sigma

Matrix defining the variances can covariances of the residual variability terms of the model. can also just supply the diagnonal parameter values (variances) as a c().

use_percent

Should RSE be reported as percent or not?

fim.calc.type

The method used for calculating the FIM. Potential values:

• 0 = Full FIM. No assumption that fixed and random effects are uncorrelated. See mftot0.

• 1 = Reduced FIM. Assume that there is no correlation in the FIM between the fixed and random effects, and set these elements in the FIM to zero. See mftot1.

• 2 = weighted models (placeholder).

• 3 = Not currently used.

• 4 = Reduced FIM and computing all derivatives with respect to the standard deviation of the residual unexplained variation (sqrt(SIGMA) in NONMEM). This matches what is done in PFIM, and assumes that the standard deviation of the residual unexplained variation is the estimated parameter (NOTE: NONMEM estimates the variance of the resudual unexplained variation by default). See mftot4.

• 5 = Full FIM parameterized with A,B,C matrices & derivative of variance. See mftot5.

• 6 = Calculate one model switch at a time, good for large matrices. See mftot6.

• 7 = Reduced FIM parameterized with A,B,C matrices & derivative of variance See mftot7.

Value

A named list of RSE values.

See Also

Other evaluate_design: evaluate.fim, evaluate_design, model_prediction, plot_efficiency_of_windows, plot_model_prediction

Examples

# NOT RUN {
## 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. 

library(PopED)

## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.md.CL

## -- 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 initial design  and design space
poped.db <- create.poped.database(ff_file="ff.PK.1.comp.oral.sd.CL",
                                  fg_file="sfg",
                                  fError_file="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=0.01,
                                  groupsize=32,
                                  xt=c( 0.5,1,2,6,24,36,72,120),
                                  minxt=0,
                                  maxxt=120,
                                  a=70)


## evaluate initial design with the reduced FIM
FIM.1 <- evaluate.fim(poped.db) 
FIM.1
det(FIM.1)
get_rse(FIM.1,poped.db)

## evaluate initial design with the full FIM
FIM.0 <- evaluate.fim(poped.db,fim.calc.type=0) 
FIM.0
det(FIM.0)
get_rse(FIM.0,poped.db,fim.calc.type=0)

## evaluate initial design with the reduced FIM 
## computing all derivatives with respect to the 
## standard deviation of the residual unexplained variation 
FIM.4 <- evaluate.fim(poped.db,fim.calc.type=4) 
FIM.4
det(FIM.4)
get_rse(FIM.4,poped.db,fim.calc.type=4)

## evaluate initial design with the full FIM with A,B,C matricies
## should give same answer as fim.calc.type=0
FIM.5 <- evaluate.fim(poped.db,fim.calc.type=5) 
FIM.5
det(FIM.5)
get_rse(FIM.5,poped.db,fim.calc.type=5)

## evaluate initial design with the reduced FIM with 
## A,B,C matricies and derivative of variance
## should give same answer as fim.calc.type=1 (default)
FIM.7 <- evaluate.fim(poped.db,fim.calc.type=7) 
FIM.7
det(FIM.7)
get_rse(FIM.7,poped.db,fim.calc.type=7)

# }