Calculate AUMC and related parameters using sparse NCA methods

The AUMC is calculated as:

Usage

pk.calc.sparse_aumc(
  conc,
  time,
  subject,
  method = "linear",
  auc.type = "AUClast",
  ...,
  options = list()
)

pk.calc.sparse_aumclast(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). 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.

method

The method for integration (one of 'lin up/log down', 'lin-log', or 'linear')

auc.type

The type of AUC to compute. Choices are 'AUCinf', 'AUClast', and 'AUCall'.

...

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 columns:

sparse_aumc

The estimated AUMC

sparse_aumc_se

Standard error of the AUMC estimate

sparse_aumc_df

Degrees of freedom for the variance estimate

Details

$$AUMC=\sum\limits_{i} w_i \overline{t_i C_i}$$

Where:

\(AUMC\)

is the estimated area under the first moment curve

\(w_i\)

is the weight applied to time i (same as for AUC, see sparse_auc_weight_linear())

\(\overline{t_i C_i}\)

is the average of the moment (time × concentration) at time i

Functions

• pk.calc.sparse_aumclast(): Compute the AUMClast for sparse PK

See also

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