nmw (version 0.1.5)

nmw-package: Understanding Nonlinear Mixed Effects Modeling for Population Pharmacokinetics

Description

This shows how NONMEM(R) <http://www.iconplc.com/innovation/nonmem/> software works.

Author

Kyun-Seop Bae <[e-mail]>

Details

This package explains 'First Order(FO) approximation' method, 'First Order Conditional Estimation(FOCE)' method, and 'Laplacian(LAPL)' method of NONMEM software.

References

1. NONMEM Users guide

2. Wang Y. Derivation of various NONMEM estimation methods. J Pharmacokinet Pharmacodyn. 2007.

3. Kang D, Bae K, Houk BE, Savic RM, Karlsson MO. Standard Error of Empirical Bayes Estimate in NONMEM(R) VI. K J Physiol Pharmacol. 2012.

4. Kim M, Yim D, Bae K. R-based reproduction of the estimation process hidden behind NONMEM Part 1: First order approximation method. 2015.

5. Bae K, Yim D. R-based reproduction of the estimation process hidden behind NONMEM Part 2: First order conditional estimation. 2016.

Examples

DataAll = Theoph
colnames(DataAll) = c("ID", "BWT", "DOSE", "TIME", "DV")
DataAll[,"ID"] = as.numeric(as.character(DataAll[,"ID"]))

nTheta = 3
nEta = 3
nEps = 2

THETAinit = c(2, 50, 0.1)
OMinit = matrix(c(0.2, 0.1, 0.1, 0.1, 0.2, 0.1, 0.1, 0.1, 0.2), nrow=nEta, ncol=nEta)
SGinit = diag(c(0.1, 0.1))

LB = rep(0, nTheta) # Lower bound
UB = rep(1000000, nTheta) # Upper bound

FGD = deriv(~DOSE/(TH2*exp(ETA2))*TH1*exp(ETA1)/(TH1*exp(ETA1) - TH3*exp(ETA3))*
             (exp(-TH3*exp(ETA3)*TIME)-exp(-TH1*exp(ETA1)*TIME)),
            c("ETA1","ETA2","ETA3"),
            function.arg=c("TH1", "TH2", "TH3", "ETA1", "ETA2", "ETA3", "DOSE", "TIME"),
            func=TRUE, hessian=TRUE)
H = deriv(~F + F*EPS1 + EPS2, c("EPS1", "EPS2"), function.arg=c("F", "EPS1", "EPS2"), func=TRUE)

PRED = function(THETA, ETA, DATAi)
{
  FGDres = FGD(THETA[1], THETA[2], THETA[3], ETA[1], ETA[2], ETA[3], DOSE=320, DATAi[,"TIME"]) 
  Gres = attr(FGDres, "gradient")
  Hres = attr(H(FGDres, 0, 0), "gradient")
  
  if (e$METHOD == "LAPL") {
    Dres = attr(FGDres, "hessian")
    Res = cbind(FGDres, Gres, Hres, Dres[,1,1], Dres[,2,1], Dres[,2,2], Dres[,3,])
    colnames(Res) = c("F", "G1", "G2", "G3", "H1", "H2", "D11", "D21", "D22", "D31", "D32", "D33") 
  } else {
    Res = cbind(FGDres, Gres, Hres)
    colnames(Res) = c("F", "G1", "G2", "G3", "H1", "H2") 
  }
  return(Res)
}

####### First Order Approximation Method # Commented out for the CRAN CPU time
#InitStep(DataAll, THETAinit=THETAinit, OMinit=OMinit, SGinit=SGinit, LB=LB, UB=UB, 
#         Pred=PRED, METHOD="ZERO")
#(EstRes = EstStep())           # 4 sec
#(CovRes = CovStep())           # 2 sec
#PostHocEta() # Using e$FinalPara from EstStep()
#TabStep()    

######## First Order Conditional Estimation with Interaction Method
#InitStep(DataAll, THETAinit=THETAinit, OMinit=OMinit, SGinit=SGinit, LB=LB, UB=UB, 
#         Pred=PRED, METHOD="COND")
#(EstRes = EstStep())           # 2 min
#(CovRes = CovStep())           # 1 min
#get("EBE", envir=e)
#TabStep()

######## Laplacian Approximation with Interacton Method
#InitStep(DataAll, THETAinit=THETAinit, OMinit=OMinit, SGinit=SGinit, LB=LB, UB=UB, 
#         Pred=PRED, METHOD="LAPL")
#(EstRes = EstStep())           # 4 min
#(CovRes = CovStep())           # 1 min
#get("EBE", envir=e)
#TabStep()