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# Non Compartmental Analysis

One of the main feature of PKanalix is the calculation of the parameters in the Non Compartmental Analysis framework.

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 calculation and the acceptance criteria. The meaning of all the settings and their default is defined here.
• The second one allows to define the global settings impacting the calculation of $$\lambda_z$$ (explained here) and the individual choice of measurements for the $$\lambda_z$$ calculation (explained here) along with the vizualization of the regression line on each subject

### NCA results

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

#### Non compartmental analysis results per individual

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

All the computed parameters depend on the type of observation and administration. All the parameters are 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.

#### Statistics on non 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.

### NCA plots

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

• Correlation between NCA 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 NCA 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.
• NCA 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.

### NCA outputs

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

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

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

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

Remarks

• To load the individual parameters using PKanalix name as headers, your just need to sheep the second line
 ncaParameters = read.table("/path/to/file.txt", sep = ",",  header = T);
ncaParameters[-1,] # to remove the CDISC name line
• To load the individual parameters using CDISC as headers, your just need to sheep the second line
 ncaParameters = read.table("/path/to/file.txt", sep = ",",  header = T, skip = 1)
• The separator is the one defined in the user preferences. We set “,” in this example as it is the one by default.