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:
- ID (mandatory): identifier of the individual
- OCCASION (formerly OCC): identifier (index) of the occasion
- TIME: time of the dose or observation record
- DATE/DAT1/DAT2/DAT3: date of the dose or observation record, to be used in combination with the TIME column
- EVENT ID (formerly EVID): identifier to indicate if the line is a dose-line or a response-line
- IGNORED OBSERVATION (formerly MDV): identifier to ignore the OBSERVATION information of that line
- CONTINUOUS COVARIATE (formerly COV): continuous covariates (which can take values on a continuous scale)
- CATEGORICAL COVARIATE (formerly CAT): categorical covariate (which can only take a finite number of values)
- REGRESSOR (formerly X): defines a regression variable, i.e a variable that can be used in the structural model (used e.g for time-varying covariates)
- IGNORE: ignores the information of that column for all lines
Column-types used for response-lines:
- OBSERVATION (mandatory, formerly Y): records the measurement/observation for continuous, count, categorical or time-to-event data
- OBSERVATION ID (formerly YTYPE): identifier for the observation type (to distinguish different types of observations, e.g PK and PD)
- CENSORING (formerly CENS): marks censored data, below the lower limit or above the upper limit of quantification
- LIMIT: upper or lower boundary for the censoring interval in case of CENSORING column
Column-types used for dose-lines:
- AMOUNT (formerly AMT): dose amount
- ADMINISTRATION ID (formerly ADM): identifier for the type of dose (given via different routes for instance)
- INFUSION RATE (formerly RATE): rate of the dose administration (used in particular for infusions)
- INFUSION DURATION (formerly TINF): duration of the dose administration (used in particular for infusions)
- ADDITIONAL DOSES (formerly ADDL): number of doses to add in addition to the defined dose, at intervals INTERDOSE INTERVAL
- INTERDOSE INTERVAL (formerly II): interdose interval for doses added using ADDITIONAL DOSES or STEADY-STATE column types
- STEADY STATE (formerly SS): marks that steady-state has been achieved, and will add a predefined number of doses before the actual dose, at interval INTERDOSE INTERVAL, in order to achieve steady-state
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:
There are three types of observations that must be tagged in the OBSERVATION column in
- 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.