#### 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

#### Models for continuous outcomes

- 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).

#### Models for non continuous outcomes

- 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.

#### Joint models for multivariate outcomes

- Continuous PKPD model learn how to implement a joint model for continuous pharmacokinetics-pharmacodynamics (PKPD) data
- Joint continuous and non continuous data model learn how to implement a joint model for continuous and non continuous data, including count, categorical and time-to-event data.

#### Models for the individual parameters

- Introduction
- Probability distribution of the individual parameters learn how to define the probability distribution and the correlation structure of the individual parameters.
- Model for individual covariates learn how to implement a model for continuous and/or categorical covariates.
- Inter occasion variability learn how to take into account inter occasion variability (IOV).
- Mixture of distributions learn how to implement a mixture of distributions for the individual parameters.

#### Pharmacokinetic models

- Single route of administration learn how to define and use a PK model for single route of administration.
- Multiple routes of administration learn how to define and use a PK model for multiple routes of administration.
- From multiple doses to steady-state learn how to define and use a PK model with multiple doses or assuming steady-state.

#### Some extensions

- Using regression variables learn how to define and use regression variables (time varying covariates).
- Bayesian estimation learn how to combine maximum likelihood estimation and Bayesian estimation of the population parameters.
- Delayed differential equations learn how to implement a model with delayed differential equations (DDE).