npde.cens.method | R Documentation |
Specifies the method used to handle censored data (data below the limit of quantification LOQ
Several methods are available to handle censored data.
pd and npde for censored data will be set to NA
for an observation ycens_ij under the LOQ, a pd_ij will be imputed in the uniform distribution [0-pLOQ_ij] where pLOQ_ij is the probability that y_ij is below LOQ, according to the model; the predictive distribution will then be used to obtain a corresponding y*_ij. This is also performed for all simulated data, and the npde are then computed on the completed dataset containing the observed y_ij for the uncensored data and the y*_ij imputed for the censored data. This method is the default.
an observation ycens_ij is replaced by the individual prediction according to the model (ipred, which must be present in the dataset). Simulated data are left untouched.
an observation ycens_ij is replaced by the population prediction according to the model. Simulated data are left untouched.
an observation ycens_ij is replaced by the value of the LOQ. Simulated data are left untouched.
This is not a function and does not have a return value, this is a statistical method.
More details can be found in the PDF documentation.
K. Brendel, E. Comets, C. Laffont, C. Laveille, and F. Mentre. Metrics for external model evaluation with an application to the population pharmacokinetics of gliclazide. Pharmaceutical Research, 23:2036–49, 2006.
## Not run:
# You need to have gridExtra installed to successfully run this example
if(requireNamespace("gridExtra", quietly=TRUE)) {
data(warfarin)
data(simwarfarinCov)
wcov<-autonpde(namobs=warfarin,namsim=simwarfarinCov, iid=1,ix=2,iy=4,icov=c(3,6:8),
namsav="warfCov", units=list(x="hr",y="mg/L", covariates=c("mg","kg","-","yr")))
# Diagnostic plots for warfarin with a covariate model
plot(wcov)
# Covariate plots
xwt.scatt<-plot(wcov, plot.type="x.scatter", covsplit=TRUE, which.cov="wt")
xwt.qqplot<-plot(wcov, plot.type="qqplot", covsplit=TRUE, which.cov="wt")
xwt.box<-plot(wcov, plot.type="covariates", which.cov="wt")
xsex.scatt<-plot(wcov, plot.type="x.scatter", covsplit=TRUE, which.cov="sex")
xsex.qqplot<-plot(wcov, plot.type="qqplot", covsplit=TRUE, which.cov="sex")
xsex.box<-plot(wcov, plot.type="covariates", which.cov="sex")
# Transforming the reference profile for npd, compared to a VPC plot
plot.tnpd<-plot(wcov, plot.type="x.scatter", ref.prof=list(id=2),
main="tnpd with reference profile ID=2")
plot.vpc<-plot(wcov, plot.type="vpc", main="VPC")
gridExtra::grid.arrange(grobs=list(plot.tnpd, plot.vpc), nrow=1, ncol=2)
}
## End(Not run)
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