Filtering a data set

Starting on the 2020 version, filtering a data set to only take a subpart into account in your modelization is possible. It allows to make filters on some specific IDs, times, measurement values,… It is also possible to define complementary filters and also filters of filters. It is accessible through the filters item on the data tab.

Creation of a filter

To create a filter, you need to click on the data set name. You can then create a “child”. It corresponds to a subpart of the data set where you will define your filtering actions.

You can see on the top (in the green rectangle) the action that you will complete and you can CANCEL, ACCEPT, or ACCEPT & APPLY with the bottoms on the bottom.

Filtering a data set: actions

In all the filtering actions, you need to define

  • An action: it corresponds to one of the following possibilities: select ids, remove ids, select lines, remove lines.
  • A header: it corresponds to the column of the data set you wish to have an action on. Notice that it corresponds to a column of the data set that was tagged with a header.
  • An operator: it corresponds to the operator of choice (=, ≠, < ≤, >, or ≥).
  • A value. When the header contains numerical values, the user can define it. When the header contains strings, a list is proposed.

For example, you can

  • Remove the ID 1 from your study:

    In that case, all the IDs except ID = 1 will be used for the study.
  • Select all the lines where the time is less or equal 24:

    In that case, all lines with time strictly greater that 24 will be removed. If a subject has no measurement anymore, it will be removed from the study.
  • Select all the ids where SEX equals F:

    In that case, all the male will be removed of the study.
  • Remove all Ids where WEIGHT less or equal 65:

    In that case, only the subjects with a weight over 65 will be kept for the study.

In any case, the interpreted filter data set will be displayed in the data tab.

Filters with several actions

In the previous examples, we only did one action. It is also possible to do several actions to define a filter. We have the possibility to define UNION and/or INTERSECTION of actions.

INTERSECTION

By clicking by the + and – button on the right, you can define an intersection of actions. For example, by clicking on the +, you can define a filter corresponding to intersection of

  • The IDs that are different to 1.
  • The lines with the time values less than 24.

Thus in that case, all the lines with a time less than 24 and corresponding to an ID different than 1 will be used in the study. If we look at the following data set as an example


Initial data set		 Resulting data set after action: select IDs ≠ 1
Considered data set for the study
as the intersection of the two actions
Resulting data set after action: select lines with time ≤ 24
UNION

By clicking by the + and – button on the bottom, you can define an union of actions. For example, in a  data set with a multi dose, I can focus on the first and the last dose. Thus, by clicking on the +, you can define a filter corresponding to union of

  • The lines where the time is strictly less than 12.
  • The lines where the time is greater than 72.


Initial data set
Resulting data set after action:
select lines where the time is strictly less than 12
Considered data set for the study
as the union of the three actions

Resulting data set after action:
select lines where the time is greater than 72

Resulting data set after action:
select lines where amt equals 40

Notice that, if just define the first two actions, all the dose lines at a time in ]12, 72[ will also be removed. Thus, to keep having all the doses, we need to add the condition of selecting the lines where the dose is defined.

In addition, it is possible to do any combination of INTERSECTION and UNION.

Other filers: filter of filter and complementary filters

Filtering a data set can be nested. Based on the definition of a filter, by clicking on the filter, it is possible to create:

  • A child: it corresponds to a new filter with the initial filter as the source data set.
  • A complement: corresponds to the complement of the filter. For example, if you defined a filter with only the IDs where the SEX is F, then the complement corresponds to the IDs where the SEX is not F.