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# Compartmental Analysis

One of the main features of PKanalix is the calculation of the parameters in the Compartmental Analysis framework. It consists in finding parameters of a model representing the PK as the dynamics in compartments for each individual. It uses the Nelder-Mead algorithm.

There is a dedicated task in the “Tasks” frame as in the following figure.

This task contains two different parts.

• The first one called “Run” corresponds to calculation button and the settings for the model and the calculation. The meaning of all the settings and their default is defined here.
• The second one allows to visualize the predictions obtained with the initial values are displayed for each individual together with the data points as explained here). It is a is very useful initial estimates before the optimization.

### CA results

When computing of the CA task is performed, it is possible to have the results in the “Results” frame. Two tables are proposed.

#### Compartmental analysis results per individual

Individual estimates of the CA parameters are displayed in the table in the tab  “INDIV. ESTIM.” part as in the following figure

All the computed parameters depend on the chosen model. Notice that on all tables, there is an icon on the top right to copy the table in a word or an excel file.

#### Statistics on compartmental analysis results

A summary table is also proposed in the tab “SUMMARY” as in the following figure

All the summary calculation is described here. Notice that on all tables, there is an icon on the top right to copy the table in a word or an excel file.

### CA plots

In the “Plots” frame, numerous plot associated to the individual parameters are displayed.

• Individual fits: The purpose of this plot is to display fit for each individual.
• Correlation between CA parameters: The purpose of this plot is to display scatter plots for each pair of parameters. It allows to identify correlations between parameters, which can be used to see the results of your analysis and see the coherence of the parameters for each individuals.
• Distribution of the CA parameters: The purpose of this plot is to see the empirical distribution of the parameters and thus have an idea of their distribution over the individuals.
• CA parameters w.r.t. covariates: The purpose of this plot is to display the individual parameters as a function of the covariates. It allows to identify correlation effects between the individual parameters and the covariates.

### CA outputs

After running the CA task, the following files are available in the result folder:

• caSummary.txt contains the summary of the CA parameters calculation, in a format easily readable by a human (but not easy to parse for a computer)
• caIndividualParametersSummary.txt contains the summary of the CA parameters in a friendly computer format.
• The first column corresponds to the name of the parameters
• The other columns correspond to the several elements describing the summary of the parameters (as explained here)
• caIndividualParameters.txt contains the CA parameters for each subject-occasion along with the covariates.
• The first line corresponds to the name of the parameters
• The other lines correspond to the value of the parameters

The files caIndividualParametersSummary.txt and caIndividualParameters.txt can be exported in R for example using the following command

 read.table("/path/to/file.txt", sep = ",", header = T)

Remark

• The separator is the one defined in the user preferences. We set “,” in this example as it is the one by default.