# Version 2018

This documentation is for Monolix Suite 2018.

©Lixoft

## Monolix

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.

## Objectives

The objectives of Monolix are to perform:

- Parameter estimation for nonlinear mixed effects models
- estimating the maximum likelihood estimator of the population parameters, without any approximation of the model (linearization, quadrature approximation, …), using the Stochastic Approximation Expectation Maximization (SAEM) algorithm,
- computing the conditional modes, sample from the conditional distribution to compute the conditional means and the conditional standard deviations of the individual parameters, using the Hastings-Metropolis algorithm
- estimating standard errors for the maximum likelihood estimator

- Model selection and diagnosis
- comparing several models using some information criteria (AIC, BIC)
- testing parameters using the Wald Test
- testing correlation using Pearson’s correlation test
- testing normality of distribution using Shapiro’s test.

- Easy description of pharmacometric models (PK, PK-PD, discrete data) with the Mlxtran language
- 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.

- Welcome
- Data
- Structural model
- Initial estimates
- Tasks and statistical model
- Results
- Plots

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