Data selection

Data set structure

The data set structure contains for each subject measurements, dose regimen, covariates etc … i.e. all collected information. The data must be in the long format, i.e each line corresponds to one individual and one time point. Different types of information (dose, observation, covariate, etc) are recorded in different columns, which must be tagged with a column type (see below). The column types are very similar and compatible with the structure used by the Nonmem software (the differences are listed here). The column-types are specified in the Data tab, when the user selects a column-type for each column of the data set as in the following picture. Datxplore often provides an initial guess of the type of the column depending on the column headers of the data set.

Description of column-types

The first line of the data set must be a header line, defining the names of the columns. The columns names are completely free. In the MonolixSuite applications, when defining the data, the user will be asked to assign each column to a column-type. The column type will indicate to the application how to interpret the information in that column. The available column types are given below:

Column-types used for all types of lines:

Column-types used for response-lines:

Column-types used for dose-lines:


The name of the outputs appearing in the Dataviewer tab are yX with X corresponding to the identifier given in the OBSERVATION ID column (for instance y1 and y2 if identifiers 1 and 2 were used in the OBSERVATION ID column). When no OBSERVATION ID column is present, the observations will be called y. Covariates appear with the same name as used in the column header name.

Loading a new data set

To load a new data set, click on “Browse”  (green highlight below) and use the pop-up window to select your data set, tag all the columns (blue highlight), and click on the button ACCEPT (purple highlight) as on the following figure:

Observation types

There are three types of observations that must be tagged in the OBSERVATION column in Data tab:

  • continuous: The observation is continuous with respect to time. For example, a concentration is a continuous observation.
  • discrete: The observation values are on a discrete scale. For example, the observation can be a categorical observation (an effect can be observed as low, medium, or high) or a count observation over a defined time (the number of epileptic crisis in a defined time).
  • event: The observation is the time elapsed until an event occurs, for example cancer recurrence.