Sparse estimators for the AUC and AUMC to the last measured concentration

These are the FUN_sparse of auclast and aumclast: with sparse PK, pk.nca() estimates those parameters from the pooled individual samples with the Bailer point estimate and the Nedelman-Jia/Holder standard error rather than integrating the arithmetic-mean profile. They wrap pk.calc.sparse_auclast() and pk.calc.sparse_aumclast(), reporting the results under the unified parameter names.

Usage

pk.calc.auclast_sparse(conc, time, subject, ..., options = list())

pk.calc.aumclast_sparse(conc, time, subject, ..., options = list())

Arguments

conc: Measured concentrations
time: Time of the measurement of the concentrations
subject: Subject identifiers (may be any class; may not be null)
...: For functions other than pk.calc.auxc, these values are passed to pk.calc.auxc
options: List of changes to the default PKNCA options (see PKNCA.options())

Value

A data.frame with the point estimate, its standard error, and the degrees of freedom, named for the parameter (auclast, auclast_se, and auclast_df, or the aumclast equivalents)

Details

The sparse variance theory is defined for the linear trapezoidal rule only, so these ignore the auc.method option; pk.nca() says so when the option is set to anything else.

Functions

pk.calc.aumclast_sparse(): Sparse AUMClast