Columns used to identify subjects-occasions

ID: subject identifier

The column is used to identify the different subjects and is mandatory. Its content is totally free (integers, double, strings…), but we recommend to use integers for better readability. The IDs will be sorted by order of appearance in the data set.

Examples

• Example with strings: the string ‘.’ will not be interpreted as a repetition of the previous line. As a consequence a data set of the form

 ID  * *
John * * 
John * * 
Mike * * 
.    * * 

contains 3 different subjects : ‘John’, ‘Mike’ and ‘.’.

• Example with mixed IDs: the lines corresponding to the same subject do not need to be next to each other. Thus, the following file contains 2 subjects with IDs “1” and “2”.

ID * *
1  * * 
1  * * 
2  * * 
2  * *
1  * *

Format restrictions

• A data set shall contain one and only one column ID.

• The ID must be defined for all lines.

• The string ‘.’ will not be interpreted as a repetition of the previous line

OCCASION (formerly OCC): occasion identifiers

Occasions define different periods of time within individuals. Each combination of subject ID and occasion will be analyzed as a separate profile in both NCA and CA. The MonolixSuite allows the definition of several columns with the column-type OCCASION, which can be used to define several levels of inter-occasion variability. The OCCASION columns can contain only integers (neither necessarily starting at one, nor necessarily consecutive), which represent occasion identifiers. All times points belonging to one occasion must be in one block (i.e., not interrupted by time points of another occasion). When switching from one occasion to the next one, time can restart at the initial value or continue. If different occasions contain time points that overlap, a washout will automatically be added when performing compartmental analysis (CA).

Examples and typical situations

• Cross over study: In that case, data are collected for each patient during two independent treatment periods of time, there is an overlap on the time definition of the periods (e.g both periods start at 0). A column-type OCCASION can be used used to identify the periods.

• Occasions without washout: In that case, there are no overlap between the periods. The time is increasing and we want to differentiate periods in terms of occasions without any reset of the dynamical system.

• Occasions with washout (due to overlapping times): In that case, the time is increasing across the occasions. When performing compartmental analysis (CA), the overlap between two time points of two different occasions creates a washout. If the washout is not desired, one of the two times can be offset by a small value to avoid the overlap. When performing non-compartmental analysis (NCA), it is not important if the overlap exists or not.

However, the following situation, which would aim at defining the same occasion index to all morning doses, is not allowed:

FAQ on occasions in the data set

• Do all the individual need to share the same sequence of occasion?
  No, the number of occasions and the times defining the occasions can differ from one individual to another.

• Do the occasion indices need to start at one for each individual? No.

• Do the occasion indices need to be consecutive for each individual? No.

• Is there any limit in terms of number of occasions? No.

• Is it possible to have several levels of occasions?
  Yes, it is possible to have several level of occasions (multiple occasion columns).

Format restrictions

• The OCCASION columns should contain only integers.

• If the OCCASION column-type is used, the OCCASION must be defined for all lines.