Monolix 2018 User guide

  1. Monolix Documentation
  2. Data and models
    1. Defining a data set
    2. Creating and using models
      1. Libraries of models
      2. Outputs and Tables
    3. Models for continuous outcomes
      1. Residual error model
      2. Handling censored (BLQ) data
      3. Mixture of structural models
    4. Models for non continuous outcomes
      1. Time-to-event data models
      2. Count data model
      3. Categorical data model
    5. Joint models for multivariate outcomes
      1. Joint models for continuous outcomes
      2. Joint models for non continuous outcomes
    6. Models for the individual parameters
      1. Introduction
      2. Probability distribution of the individual parameters
      3. Model for individual covariates
      4. Inter occasion variability (IOV)
      5. Mixture of distributions
    7. Pharmacokinetic models
      1. PK model: single route of administration
      2. PK model: multiple routes of administration
      3. From multiple doses to steady-state
    8. Extensions
      1. Using regression variables
      2. Bayesian estimation
      3. Delayed differential equations
  3. Tasks
    1. Initialization
    2. Population parameter estimation using SAEM
    3. Conditional distribution
    4. EBEs
    5. Standard error using the Fisher Information Matrix
    6. Log Likelihood estimation
    7. Algorithms convergence assessment
    8. What result files are generated by Monolix?
    9. Tests
    10. Monolix API
      1. API concerning the covariate models
      2. API concerning the observation models
      3. API concerning the population parameters
      4. API concerning the individual parameter models
      5. API concerning the scenario
      6. API concerning the results
      7. API concerning the project management
      8. API concerning the settings
  4. Plots
    1. Data
      1. Observed data
    2. Model for the observations
      1. Individual fits
      2. Observation versus Prediction
      3. Scatter plot of the residuals
      4. Distribution of the residuals
    3. Model for the individual parameters
      1. Distribution of the individual parameters
      2. Distribution of the random effects
      3. Correlation between the random effects
      4. Individual parameters versus covariates
    4. Predictive checks and predictions
      1. Visual predictive checks
      2. Numerical predictive checks
      3. BLQ predictive checks
      4. Prediction distribution
    5. Tasks results
      1. Likelihood contribution
      2. Standard errors of the estimates
    6. Convergence diagnosis
      1. SAEM
      2. MCMC
      3. Importance sampling
    7. Export charts
  5. FAQ
    1. Evolutions from Monolix2016R1 to Monolix2018R1
    2. Submission of Monolix analysis to regulatory agencies
    3. Running Monolix using a command line
    4. How to compute AUC using in Monolix and Mlxtran
    5. How to export to Datxplore, Mlxpore and Simulx ?

1.Monolix Documentation

Version 2019

This documentation is for Monolix starting from 2018 version.


Monolix (Non-linear mixed-effects models or “MOdèles NOn LInéaires à effets miXtes” in French) is a platform of reference for model based drug development. It combines the most advanced algorithms with unique ease of use. Pharmacometricians of preclinical and clinical groups can rely on Monolix for population analysis and to model PK/PD and other complex biochemical and physiological processes. Monolix is an easy, fast and powerful tool for parameter estimation in non-linear mixed effect models, model diagnosis and assessment, and advanced graphical representation. Monolix is the result of a ten years research program in statistics and modeling, led by Inria (Institut National de la Recherche en Informatique et Automatique) on non-linear mixed effect models for advanced population analysis, PK/PD, pre-clinical and clinical trial modeling & simulation.


The objectives of Monolix are to perform:

  1. Parameter estimation for nonlinear mixed effects models
  2. Model selection and diagnosis
  3. Easy description of pharmacometric models (PK, PK-PD, discrete data) with the Mlxtran language
  4. Goodness of fit plots

An interface for ease of use

Monolix can be used either via a graphical user interface (GUI) or a command-line interface (CLI) for powerful scripting. This means less programming and more focus on exploring models and pharmacology to deliver in time. The interface is depicted as follows:

The GUI consists of 7 tabs.

Each of these tabs refer to a specific section on this website. An advanced description of available plots is also provided.

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