Summarize PKNCA results

Summarize PKNCA results

Usage

# S3 method for class 'PKNCAresults'
summary(
  object,
  ...,
  drop_group = object$data$conc$columns$subject,
  drop_param = character(),
  summarize_n = NA,
  not_requested = ".",
  not_calculated = "NC",
  drop.group = deprecated(),
  summarize.n.per.group = deprecated(),
  not.requested.string = deprecated(),
  not.calculated.string = deprecated(),
  pretty_names = NULL,
  caption_prefix = NULL
)

Arguments

object

The results to summarize

...

Ignored.

drop_group

Which group(s) should be dropped from the formula?

drop_param

Which parameters should be excluded from the summary?

summarize_n

Should a column for N be added (TRUE or FALSE)? NA means to automatically detect adding N if the data has a subject column indicated. Note that N is maximum number of parameter results for any parameter; if no parameters are requested for a group, then N will be NA.

not_requested

A character string to use when a parameter summary was not requested for a parameter within an interval.

not_calculated

A character string to use when a parameter summary was requested, but the point estimate AND spread calculations (if applicable) returned NA. It is described in the caption when it is used.

drop.group, summarize.n.per.group, not.requested.string, not.calculated.string

Deprecated use drop_group, not_requested, not_calculated, or summarize_n, instead

pretty_names

Should pretty names (easier to understand in a report) be used? TRUE is yes, FALSE is no, and NULL is yes if units are used and no if units are not used.

caption_prefix

Prefix to prepend to the generated table caption.

Value

A data frame of NCA parameter results summarized according to the summarization settings.

Details

Excluded results will not be included in the summary.

See also

PKNCA.set.summary(), print.summary_PKNCAresults()

Examples

conc_obj <- PKNCAconc(as.data.frame(datasets::Theoph), conc ~ Time | Subject)
d_dose <-
  unique(datasets::Theoph[
    datasets::Theoph$Time == 0,
    c("Dose", "Time", "Subject")
  ])
dose_obj <- PKNCAdose(d_dose, Dose ~ Time | Subject)
data_obj_automatic <- PKNCAdata(conc_obj, dose_obj)
results_obj_automatic <- pk.nca(data_obj_automatic)
# To get standard results run summary
summary(results_obj_automatic)
#>  start end  N     auclast        cmax               tmax                 tlag
#>      0 Inf 12 98.7 [22.5] 8.65 [17.0] 1.14 [0.630, 3.55] 0.000 [0.000, 0.000]
#>         count_conc   half.life aucinf.obs aucpext.obs        cl.obs
#>  11.0 [11.0, 11.0] 8.18 [2.12] 115 [28.4] 13.8 [6.34] 0.0398 [29.4]
#> 
#> Caption: auclast, cmax, aucinf.obs, cl.obs: geometric mean and geometric coefficient of variation; tmax, tlag, count_conc: median and range; half.life, aucpext.obs: arithmetic mean and standard deviation; N: number of subjects
#> 
# To enable numeric conversion and extraction, do not give a spread function
# and subsequently run as.numeric on the result columns.
PKNCA.set.summary(
  name = c("auclast", "cmax", "half.life", "aucinf.obs"),
  point = business.geomean,
  description = "geometric mean"
)
PKNCA.set.summary(
  name = c("tmax"),
  point = business.median,
  description = "median"
)
summary(results_obj_automatic, not_requested = "NA")
#>  start end  N auclast cmax tmax                 tlag        count_conc
#>      0 Inf 12    98.7 8.65 1.14 0.000 [0.000, 0.000] 11.0 [11.0, 11.0]
#>  half.life aucinf.obs aucpext.obs        cl.obs
#>       7.99        115 13.8 [6.34] 0.0398 [29.4]
#> 
#> Caption: auclast, cmax, half.life, aucinf.obs: geometric mean; tmax: median; tlag, count_conc: median and range; aucpext.obs: arithmetic mean and standard deviation; cl.obs: geometric mean and geometric coefficient of variation; N: number of subjects
#>