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

get_rse(
  fim,
  poped.db,
  bpop = poped.db$parameters$bpop[, 2],
  d = poped.db$parameters$d[, 2],
  docc = poped.db$parameters$docc,
  sigma = poped.db$parameters$sigma,
  use_percent = TRUE,
  fim.calc.type = poped.db$settings$iFIMCalculationType,
  prior_fim = poped.db$settings$prior_fim,
  ...
)

Arguments

fim

A Fisher Information Matrix (FIM).

poped.db

A PopED database.

bpop

A vector containing the values of the fixed effects used to compute the fim.

d

A vector containing the values of the diagonals of the between subject variability matrix.

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 diagonal parameter values (variances) as a c().

use_percent

Should RSE be reported as percent?

fim.calc.type

The method used for calculating the FIM. Potential values:

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

  • 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.

  • 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 residual unexplained variation by default).

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

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

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

prior_fim

A prior FIM to be added to the fim. Should be the same size as the fim.

...

Additional arguments passed to inv.

Value

A named list of RSE values. If the estimated parameter is assumed to be zero then for that parameter the standard error is returned.

See also

Examples

## 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
#> function (model_switch, xt, parameters, poped.db) #> { #> with(as.list(parameters), { #> y = xt #> N = floor(xt/TAU) + 1 #> y = (DOSE * Favail/V) * (KA/(KA - CL/V)) * (exp(-CL/V * #> (xt - (N - 1) * TAU)) * (1 - exp(-N * CL/V * TAU))/(1 - #> exp(-CL/V * TAU)) - exp(-KA * (xt - (N - 1) * TAU)) * #> (1 - exp(-N * KA * TAU))/(1 - exp(-KA * TAU))) #> return(list(y = y, poped.db = poped.db)) #> }) #> } #> <bytecode: 0x7fe25cadfcb0> #> <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 initial design and design space 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), notfixed_bpop=c(CL=1,V=1,KA=1,Favail=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
#> [,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)
#> [1] 5.996147e+22
det(FIM.1)^(1/7)
#> [1] 1794.658
get_rse(FIM.1,poped.db)
#> CL V KA d_CL d_V d_KA SIGMA[1,1] #> 4.738266 2.756206 13.925829 25.627205 30.344316 25.777327 11.170784
## evaluate initial design with the full FIM FIM.0 <- evaluate.fim(poped.db,fim.calc.type=0) FIM.0
#> [,1] [,2] [,3] [,4] [,5] #> [1,] 47625.234145 -341.996566 35.504624 -2.073844e+03 -5899.486674 #> [2,] -341.996566 30.887205 -12.589615 -1.686280e+01 -54.629529 #> [3,] 35.504624 -12.589615 452.758773 -8.336530e-01 -43.619195 #> [4,] -2073.844369 -16.862802 -0.833653 3.107768e+03 10.728786 #> [5,] -5899.486674 -54.629529 -43.619195 1.072879e+01 27307.089308 #> [6,] 4.490538 -6.550313 18.653863 2.613561e-02 3.265608 #> [7,] -54419.723543 -1070.933661 2955.924225 5.215403e+02 11214.210707 #> [,6] [,7] #> [1,] 4.49053810 -54419.72354 #> [2,] -6.55031322 -1070.93366 #> [3,] 18.65386273 2955.92423 #> [4,] 0.02613561 521.54030 #> [5,] 3.26560786 11214.21071 #> [6,] 41.81083599 71.08764 #> [7,] 71.08763896 806176.95068
det(FIM.0)
#> [1] 1.220371e+24
det(FIM.0)^(1/7)
#> [1] 2760.117
get_rse(FIM.0,poped.db)
#> CL V KA d_CL d_V d_KA SIGMA[1,1] #> 3.560994 2.560413 4.811952 26.270324 30.901555 26.503936 12.409516
## 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
#> [,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 1.043081e+02 2242.842141 14.21752779 #> [,7] #> [1,] 0.00000 #> [2,] 0.00000 #> [3,] 0.00000 #> [4,] 104.30806 #> [5,] 2242.84214 #> [6,] 14.21753 #> [7,] 32247.07803
det(FIM.4)
#> [1] 2.398459e+21
get_rse(FIM.4,poped.db,fim.calc.type=4)
#> CL V KA d_CL d_V d_KA SIGMA[1,1] #> 4.738266 2.756206 13.925829 25.627205 30.344316 25.777327 5.585392
## 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
#> [,1] [,2] [,3] [,4] [,5] #> [1,] 47625.234145 -341.996566 35.504624 -2.073844e+03 -5899.486674 #> [2,] -341.996566 30.887205 -12.589615 -1.686280e+01 -54.629529 #> [3,] 35.504624 -12.589615 452.758773 -8.336530e-01 -43.619195 #> [4,] -2073.844369 -16.862802 -0.833653 3.107768e+03 10.728786 #> [5,] -5899.486674 -54.629529 -43.619195 1.072879e+01 27307.089308 #> [6,] 4.490538 -6.550313 18.653863 2.613561e-02 3.265608 #> [7,] -54419.723543 -1070.933661 2955.924225 5.215403e+02 11214.210707 #> [,6] [,7] #> [1,] 4.49053810 -54419.72354 #> [2,] -6.55031322 -1070.93366 #> [3,] 18.65386273 2955.92423 #> [4,] 0.02613561 521.54030 #> [5,] 3.26560786 11214.21071 #> [6,] 41.81083599 71.08764 #> [7,] 71.08763896 806176.95068
det(FIM.5)
#> [1] 1.220371e+24
get_rse(FIM.5,poped.db,fim.calc.type=5)
#> CL V KA d_CL d_V d_KA SIGMA[1,1] #> 3.560994 2.560413 4.811952 26.270324 30.901555 26.503936 12.409516
## 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
#> [,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.7)
#> [1] 5.996147e+22
get_rse(FIM.7,poped.db,fim.calc.type=7)
#> CL V KA d_CL d_V d_KA SIGMA[1,1] #> 4.738266 2.756206 13.925829 25.627205 30.344316 25.777327 11.170784
## evaluate FIM and rse with prior FIM.1 poped.db.prior = create.poped.database(poped.db, prior_fim = FIM.1) FIM.1.prior <- evaluate.fim(poped.db.prior) all.equal(FIM.1.prior,FIM.1) # the FIM is only computed from the design in the poped.db
#> [1] TRUE
get_rse(FIM.1.prior,poped.db.prior) # the RSE is computed with the prior information
#> CL V KA d_CL d_V d_KA SIGMA[1,1] #> 3.350460 1.948932 9.847048 18.121170 21.456671 18.227322 7.898937