Writing PKNCA Parameter Functions

The PKNCA package is designed to be comprehensive in its coverage of the needs of an noncompartmental analysis (NCA) specialist. While it has many NCA parameters specified, it may not have all parameters defined, and its design is modular to accept new parameter definitions. From its inception, PKNCA is built in modules to allow addition of new components (or removal of unnecessary ones). Defining new NCA parameters is straight-forward, and this guide will describe how it is done. The three parts to writing a new NCA parameter in PKNCA are described below.

Writing the Parameter Function

Requirements

The starting point to writing a new NCA parameter is writing the function that calculates the parameter value. The function can be passed any of the following arguments. The arguments must be named as described below:

conc is the numeric vector of plasma concentrations for an interval for a single group (usually a single analyte for a single subject in a single study).
time is the numeric vector of the time for plasma concentration measurements.
duration.conc is the duration of a concentration measurement (usually for urine or fecal measurements)
dose is the numeric vector of dose amounts for an interval for a single group. NOTE: This is a vector and not always a scalar. If your function expects a scalar, you should usually take the sum of the dose argument.
time.dose is the numeric vector of time for the doses.
duration.dose is the duration of a dose (usually for intravenous infusions)
start and end are the scalar numbers for the start and end time of the current interval. NOTE: end may be Inf (infinity).
options are the PKNCA options used for the current calculation usually as defined by the PKNCA.option function (though these options may be over-ridden by the options argument to the PKNCAdata function.
Or, any NCA parameters by name (as given by names(get.interval.cols())).

The function should return either a scalar which is the value for the parameter (usually the case) or a data.frame with parameters named for each parameter calculated. For an example of returning a data.frame, see the half.life function.

The return value may have an attribute of exclude (set by attr(return_value, "exclude") <- "reason"). If the exclude attribute is set to a character string, then that string will be included in the exclude column for results. If any of the input parameters have an exclude attribute set, then those are also added to the exclude column. The exception to the setting of the exclude column is if the exclude attribute is "DO NOT EXCLUDE", then the exclude column is set to NA_character_.

Best Practices

Use the function assert_conc_time if the function takes either conc or time as an input.
Make sure that you check for missing values (NA) in your inputs.
Don’t recalculate other NCA parameters within your function unless you absolutely must. Take the NCA parameter as an input. That way, PKNCA will track the calculation dependencies.
For consistency with the rest of PKNCA, start the function name with “pk.calc” (like “pk.calc.cmax”).

Tell PKNCA about the Parameter

Just writing a function doesn’t connect it to the rest of PKNCA. You have to tell PKNCA that the function exists and a few more details about it. To do this, you need to use the add.interval.col function. The function takes up to seven arguments:

name is the name of the parameter (as a character string).
FUN is the function name (as a character string).
values are the possible values for the interval column (currently only TRUE and FALSE are supported).
depends is a character vector of columns that must exist before this column can be created. Use this to tell PKNCA about calculation dependencies (parameter X must be calculated to be able to calculate parameter Y).
formalsmap remaps the (formal) function arguments. formalsmap is usually used when the same function may be used for multiple different parameters, for example the function pk.calc.thalf.eff is used to calculate the parameters thalf.eff.obs, thalf.eff.pred, thalf.eff.last, thalf.eff.iv.obs, thalf.eff.iv.pred, and thalf.eff.iv.last with different mean residence time inputs.
desc is a text description of the parameter.

Tell PKNCA How to Summarize the Parameter

For any parameter, PKNCA needs to know how to summarize it for the summary function of the PKNCAresults class. To tell PKNCA how to summarize a parameter, use the PKNCA.set.summary function. It takes at least these four arguments:

name must match an already existing parameter name (added by the add.interval.col function).
description is a human-readable description of the point and spread for use in table captions.
point is the function to calculate the point estimate (called as point(x), and it must return a scalar).
spread is the function to calculate the spread (or variability). The function will be called as spread(x) and must return a scalar or a two-long vector.

Putting It Together

One of the most common examples is the function to calculate C_max:

#' Determine maximum observed PK concentration
#'
#' @inheritParams assert_conc_time
#' @param check Run \code{\link{assert_conc_time}}?
#' @return a number for the maximum concentration or NA if all
#' concentrations are missing
#' @export
pk.calc.cmax <- function(conc, check=TRUE) {
  if (check)
    assert_conc_time(conc=conc)
  if (length(conc) == 0 | all(is.na(conc))) {
    NA
  } else {
    max(conc, na.rm=TRUE)
  }
}
## Add the column to the interval specification
add.interval.col("cmax",
                 FUN="pk.calc.cmax",
                 values=c(FALSE, TRUE),
                 unit_type="conc",
                 pretty_name="Cmax",
                 desc="Maximum observed concentration",
                 depends=c())
PKNCA.set.summary("cmax", "geometric mean and geometric coefficient of variation", business.geomean, business.geocv)