monolix.lixoft.comMonolix documentation - LIXOFT

Overview Data Defining a data set Data Format Data formatting Data formatting presets Filtering a data set Handling censored data Mapping between data and model Structural Model Libraries of models Writing your own model Continuous outcomes Typical PK models Single route of administration Multiple routes of administration Multiple doses to steady-state Mixture of structural models Joint models for continuous output Non-continuous outcomes Time-to-event data models Count data model Categorical data model Joint models for non continuous outcomes Extensions Using a scale factor Using regression variables Delayed differential equations Outputs and Tables Compute AUC and Cmax Statistical Model Overview Observation (error) model Individual Model Overview Distribution of indiv. param. Covariate model Inter-occasion variability Mixture of distributions Tasks and results Overview Estimation Tasks Initialization Initial values and method Check initial estimates and auto init Pop. parameters SAEM Bayesian estimation or fixed pop. parameters EBEs Cond. distribution Conditional distribution Statistical tests Proposal Standard errors Likelihood Cv. assessment Model building Overview Automatic covariate model building Statistical model building with SAMBA Projects Comparison in Sycomore Bootstrap Output files Comments Plots Overview Generating and exporting plots Interacting with the plots Transferring plot formatting Export charts data Data Observed data Covariate Viewer Bivariate Data Viewer Model for observations Individual fits Obs. vs Pred. Scatter plot of residuals Distribution of residuals Model for indiv. param. Distr. of indiv. param. Distr. of random effects Corr. between rand. effects Indiv. param vs covariates Predictive checks Visual predictive check Numerical predictive check BLQ predictive check Prediction distribution Conv. diagnosis SAEM MCMC Importance sampling Task results Likelihood contribution Stand. errors of estimates Reporting Overview List of reporting keywords List of plot settings List of table settings -functions R-package installation and initialization Examples using R functions List of R functions to run Monolix Rsmlx Demos & Case studies Demo projects Case studies FAQ Frequent Questions List of known bugs Feedback Single page PDF documentation Select Page Monolix Documentation Version 2024 This documentation is for Monolix starting from 2018 version. ©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. Overview Data Structural Model Statistical Model Tasks and results Plots Reporting -functions Demos & Case studies FAQ Single page PDF documentation RSS A web site of the network Lixoft.com | Follow us on Linkedin Sycomore Monolix PKanalix Dataset Simulx Mlxtran Lixoft...