This is a structural model function that encodes a model that is one-compartment, oral absorption, single bolus dose, parameterized using CL. The function is suitable for input to the create.poped.database function using the ff_file argument.

ff.PK.1.comp.oral.sd.CL(model_switch, xt, parameters, poped.db)

Arguments

model_switch

a vector of values, the same size as xt, identifying which model response should be computed for the corresponding xt value. Used for multiple response models.

xt

a vector of independent variable values (often time).

parameters

A named list of parameter values.

poped.db

a poped database. This can be used to extract information that may be needed in the model file.

Value

A list consisting of:

  1. y the values of the model at the specified points.

  2. poped.db A (potentially modified) poped database.

See also

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: 0x7fe20a979808> #> <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) ##################################### ## create plot of model without variability plot_model_prediction(poped.db)
## evaluate initial design FIM <- evaluate.fim(poped.db) FIM
#> [,1] [,2] [,3] [,4] [,5] [,6] #> [1,] 19821.28445 -21.836551 -8.622140 0.000000e+00 0.000000 0.00000000 #> [2,] -21.83655 20.656071 -1.807099 0.000000e+00 0.000000 0.00000000 #> [3,] -8.62214 -1.807099 51.729039 0.000000e+00 0.000000 0.00000000 #> [4,] 0.00000 0.000000 0.000000 3.107768e+03 10.728786 0.02613561 #> [5,] 0.00000 0.000000 0.000000 1.072879e+01 27307.089308 3.26560786 #> [6,] 0.00000 0.000000 0.000000 2.613561e-02 3.265608 41.81083599 #> [7,] 0.00000 0.000000 0.000000 5.215403e+02 11214.210707 71.08763896 #> [,7] #> [1,] 0.00000 #> [2,] 0.00000 #> [3,] 0.00000 #> [4,] 521.54030 #> [5,] 11214.21071 #> [6,] 71.08764 #> [7,] 806176.95068
det(FIM)
#> [1] 5.996147e+22
get_rse(FIM,poped.db)
#> CL V KA d_CL d_V d_KA sig_prop #> 4.738266 2.756206 13.925829 25.627205 30.344316 25.777327 11.170784