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Data and models

Creating and using models

  • Libraries of models      learn how to use the Monolix libraries of PKPD models and create your own libraries
  • Model implementation      learn how to create a project by defining the model using the Graphical User Interface (GUI) and/or an external model file
  • Outputs and Tables     learn how to define outputs and create tables with selected outputs of the model
  • Residual error model      learn how to use the predefined residual error models
  • Handling censored data      learn how to handle easily and properly censored data, i.e. data below (resp. above) a lower (resp.upper) limit of quantification (LOQ) or detection (LOD).
  • Mixture of structural models      learn how to implement between subject mixture models (BSMM) and within subject mixture models (WSMM).
  • Time-to-event data model      learn how to implement a model for (repeated) time-to-event data
  • Count data model      learn how to implement a model for count data, including hidden Markov model.
  • Categorical data model      learn how to implement a model for categorical data, assuming either independence or a Markovian dependence between observations.