PopED (version 0.3.2)

calc_ofv_and_fim: Calculate the Fisher Information Matrix (FIM) and the OFV(FIM) for either point values or parameters or distributions.

Description

This function computes the expectation of the FIM and OFV(FIM) for either point values of parameter estimates or parameter distributions given the model, parameters, distributions of parameter uncertainty, design and methods defined in the PopED database.

Usage

calc_ofv_and_fim(poped.db, ofv = 0, fim = 0,
  d_switch = poped.db$settings$d_switch,
  bpopdescr = poped.db$parameters$bpop, ddescr = poped.db$parameters$d,
  bpop = bpopdescr[, 2, drop = F], d = getfulld(ddescr[, 2, drop = F],
  poped.db$parameters$covd), docc_full = getfulld(poped.db$parameters$docc[,
  2, drop = F], poped.db$parameters$covdocc),
  model_switch = poped.db$design$model_switch, ni = poped.db$design$ni,
  xt = poped.db$design$xt, x = poped.db$design$x, a = poped.db$design$a,
  fim.calc.type = poped.db$settings$iFIMCalculationType,
  use_laplace = poped.db$settings$iEDCalculationType, laplace.fim = FALSE,
  ofv_fun = poped.db$settings$ofv_fun, evaluate_fim = TRUE, ...)

Arguments

poped.db

A PopED database.

ofv

The current ofv. If other than zero then this values is simply returned unchanged.

fim

The current FIM. If other than zero then this values is simply returned unchanged.

d_switch

• ******START OF CRITERION SPECIFICATION OPTIONS**********

D-family design (1) or ED-familty design (0) (with or without parameter uncertainty)

bpopdescr

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).

ddescr

Matrix defining the diagnonals of the IIV (same logic as for the bpopdescr).

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_full

A between occasion variability matrix.

model_switch

A matrix that is the same size as xt, specifying which model each sample belongs to.

ni

A vector of the number of samples in each group.

xt

A matrix of sample times. Each row is a vector of sample times for a group.

x

A matrix for the discrete design variables. Each row is a group.

a

A matrix of covariates. Each row is a group.

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.

use_laplace

Should the Laplace method be used in calculating the expectation of the OFV?

laplace.fim

Should an E(FIM) be calculated when computing the Laplace approximated E(OFV). Typically the FIM does not need to be computed and, if desired, this calculation is done usng the standard MC integration technique, so can be slow.

ofv_fun

User defined function used to compute the objective function. The function must have a poped database object as its first argument and have "..." in its argument list. Can be referenced as a function or as a file name where the funciton defined in the file has the same name as the file. e.g. "cost.txt" has a function named "cost" in it.

evaluate_fim

Should the FIM be calculated?

...

Other arguments passed to the function.

Value

A list containing the FIM and OFV(FIM) or the E(FIM) and E(OFV(FIM)) according to the function arguments.

See Also

Other E-family: ed_laplace_ofv, ed_mftot, evaluate.e.ofv.fim

Other evaluate_FIM: evaluate.e.ofv.fim, evaluate.fim, ofv_fim

Other FIM: LinMatrixH, LinMatrixLH, LinMatrixL_occ, ed_laplace_ofv, ed_mftot, efficiency, evaluate.e.ofv.fim, evaluate.fim, gradf_eps, mf3, mf5, mf6, mf7, mf8, mftot0, mftot1, mftot2, mftot3, mftot4, mftot5, mftot6, mftot7, mftot, mf, ofv_criterion, ofv_fim

Examples

# NOT RUN {
library(PopED)

############# START #################
## Create PopED database
## (warfarin model for optimization
##  with parameter uncertainty)
#####################################

## 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. 

## Optimization using an additive + proportional reidual error
## to avoid sample times at very low concentrations (time 0 or very late samoples).

## find the parameters that are needed to define from the structural model
ff.PK.1.comp.oral.sd.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) 
}

# Adding 10% log-normal Uncertainty to fixed effects (not Favail)
bpop_vals <- c(CL=0.15, V=8, KA=1.0, Favail=1)
bpop_vals_ed_ln <- cbind(ones(length(bpop_vals),1)*4, # log-normal distribution
                         bpop_vals,
                         ones(length(bpop_vals),1)*(bpop_vals*0.1)^2) # 10% of bpop value
bpop_vals_ed_ln["Favail",]  <- c(0,1,0)
bpop_vals_ed_ln

## -- 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.add.prop",
                                  bpop=bpop_vals_ed_ln, 
                                  notfixed_bpop=c(1,1,1,0),
                                  d=c(CL=0.07, V=0.02, KA=0.6), 
                                  sigma=c(0.01,0.25),
                                  groupsize=32,
                                  xt=c( 0.5,1,2,6,24,36,72,120),
                                  minxt=0,
                                  maxxt=120,
                                  a=70,
                                  mina=0,
                                  maxa=100)

############# END ###################
## Create PopED database
## (warfarin model for optimization
##  with parameter uncertainty)
#####################################


calc_ofv_and_fim(poped.db)

# }
# NOT RUN {
  
  calc_ofv_and_fim(poped.db,d_switch=0)
  calc_ofv_and_fim(poped.db,d_switch=0,use_laplace=TRUE)
  calc_ofv_and_fim(poped.db,d_switch=0,use_laplace=TRUE,laplace.fim=TRUE)

# }