Noncompartmental Analysis
In PKPDmisc: Pharmacokinetic and Pharmacodynamic Data Management Functions

For initial analysis, or in times where more complex analysis - such as nonlinear mixed effects modeling - is unnecessary, noncompartmental analysis is frequently used. While there are R packages, such as metrumrg or PK packages, the functionality is too complex and does not provide the flexibility to easily perform most of the basic functions needed without massaging or wringing your data into the necessary formats. The NCA functions in PKPDmisc are designed to be robust, but generalizable, allowing multiple data designs and software to be easily utilized.

In addition, while it is possible to use these functions in base R, it is highly recommended to use dplyr to supplement much of the surrounding data manipulation for both speed and clarity.

While other resources are available to get started with dplyr, briefly, the general workflow will be as follows:

take raw data
filter unnecessary data
group data
use summarize or mutate for the calculations.

However, there are certain analyses that are frequently used enough that PKPDmisc provides wrapper functions to reduce the above steps to a single line of code.

For those getting accustomed to R, keep in mind that there is also simple examples provided with each function that are available by doing ?<function>, so for example ?auc_partial.

For the examples, the sd_oral_richpk dataset from PKPDdatasets will be used.

To install, run devtools::install_github("dpastoor/PKPDdatasets")

library("PKPDmisc")
library("dplyr")
pkdat <- sd_oral_richpk
head(pkdat)

Common summary functions

For a number of tasks that are commonly run, summary functions reduce the number of steps necessary. While I will not highlight each here, a quick glance at the package source code can show which functions are available. The API follows that functions named s_<function> will provide these multiple steps at once.

In general, each function is designed to take a grouped dataframe from dplyr to provide the function insight as to how to handle the overall data, so in general the analysis will will look like:

df %>% group_by(<grouping var>) %>% s_<function>()

The first such function is s_pauc(). This function can give multiple partial auc values, with each column being named pAUC<tfirst>_<tlast>. Multiple partial AUC slices can be requested by passing a list of requested times.

pkdat %>% group_by(ID) %>% 
  s_pauc(Time, Conc, list(c(0, 8), c(0, 24), c(8, 24))) %>%
  head

Similar to dplyr and ggplot2 the summary functions use non-standard evaluation (no quotes required), however this is not conducive to programming or for use inside of other functions. As such, a standard evaluation version is provided with the nomenclature s_<function>_

paucs <- pkdat %>% group_by(ID) %>% 
  s_pauc_("Time", "Conc", list(c(0, 8), c(0, 24), c(8, 24)), digits =1)
head(paucs)

Often once AUC or cmax statistics have been calculated, quantiles are calculated to create quantile plots or other summary evaluations. Here, the s_quantiles function can be used.

paucs %>% ungroup() %>% s_quantiles(pAUC0_24, probs = c(0, 0.25, 0.5, 0.75, 1))
paucs %>% ungroup() %>% s_quantiles_("pAUC0_24", probs = c(0, 0.25, 0.5, 0.75, 1))