Sparse AUC and AUMC for IV bolus dosing with C0 back-extrapolation

These are the FUN_sparse of aucivlast, aucivall, aucivinf.obs, aumcivlast, aumcivall, and aumcivinf.obs: with sparse PK and no concentration at time 0, \(C_0\) is back-extrapolated from the mean profile with the methods of pk.calc.c0() (log-linear from the first two means when the profile declines), and the area from time 0 to the first sample is added to the sparse AUC (see pk.calc.auclast_sparse(), pk.calc.sparse_auc() for AUCall, and pk.calc.aucinf.obs_sparse()). \(C_0\) is a function of the means, so its uncertainty is added to the standard error with the delta method. With the linear trapezoidal rule, the AUMC from time 0 to the first sample does not depend on \(C_0\). A nonzero concentration measured at time 0 is used as \(C_0\); a zero there, measured or imputed (as by the start_conc0 imputation), is not, as for pk.calc.c0().

Usage

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

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

pk.calc.aucivinf.obs_sparse(
  conc,
  time,
  subject,
  lambda.z,
  lambda.z.time.first,
  lambda.z.time.last,
  lambda.z.n.points,
  ...,
  options = list()
)

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

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

pk.calc.aumcivinf.obs_sparse(
  conc,
  time,
  subject,
  lambda.z,
  lambda.z.time.first,
  lambda.z.time.last,
  lambda.z.n.points,
  ...,
  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). A missing subject marks an imputed concentration (such as the zero that the start_conc0 imputation adds at the start of an interval): a time where every subject is missing has a known concentration, which enters sparse estimates but not their variance.

...

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())

lambda.z

The elimination rate of the mean profile

lambda.z.time.first, lambda.z.time.last, lambda.z.n.points

The first and last time and the number of points of the half-life fit to the mean profile

Value

A data.frame with the point estimate, its standard error, and the degrees of freedom, named for the parameter (for example, aucivlast, aucivlast_se, and aucivlast_df)

Functions

• pk.calc.aucivall_sparse(): Sparse AUCall for IV bolus dosing

• pk.calc.aucivinf.obs_sparse(): Sparse AUCinf,obs for IV bolus dosing

• pk.calc.aumcivlast_sparse(): Sparse AUMClast for IV bolus dosing

• pk.calc.aumcivall_sparse(): Sparse AUMCall for IV bolus dosing

• pk.calc.aumcivinf.obs_sparse(): Sparse AUMCinf,obs for IV bolus dosing

See also

Other Sparse Methods: as_sparse_pk(), pk.calc.aucinf.obs_sparse(), pk.calc.auclast_sparse(), pk.calc.sparse_auc(), pk.calc.sparse_aumc(), sparse_auc_weight_linear(), sparse_bootstrap(), sparse_mean(), summary.PKNCAresults_sparse_bootstrap()