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Check that grouping specified to PKNCA is likely to be correct If the grouping results in most groups having too few datapoints to perform NCA and calculate half-life, then the grouping is likely wrong.

check_nca_grouping.Rd

Check that grouping specified to PKNCA is likely to be correct If the grouping results in most groups having too few datapoints to perform NCA and calculate half-life, then the grouping is likely wrong.

Usage

check_nca_grouping(
  data,
  groups,
  dictionary,
  settings = list(),
  threshold = 0.7,
  verbose = TRUE
)

Arguments

data

dataset in pre-parse format, preferrably created using create_nca_data(). Can also be a NONMEM-style dataset with EVID=0|1 column indicating PK an dosing data.

groups

optional grouping variable, e.g. ACTARM.

dictionary

abridged data dictionary for the input dataset, specified as list object. Requires at least entries for subject_id, time, conc, dose. Optional entries are: visit. If required entries are specified only partially, then default values (i.e. CDISC) nomenclature will be imputed for missing identifiers.

settings

list of settings for NCA. Provided setting names can either be settings recognized directly by pknca, or settings that are included in the map provided by nca_settings_map() (which are then mapped to pknca setting names). In addition to standard PKNCA settings, the following settings are handled internally and not passed to PKNCA:

  • min.hl.time (or minHalfLifeTime): minimum time required for a data point to be eligible for the lambda-z (terminal slope) calculation. Any data points with time < this value are excluded from the lambda-z fit (but remain in the analysis for other parameters such as Cmax and AUClast). This is implemented by setting exclude_lambda_z flags internally.

threshold

the fraction of data to be allowed to have too few data points for NCA.

verbose

verbose output?

Value

TRUE or FALSE