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PKanalix
Monolix
Simulx
R API
Getting Started
Tutorials

Main

PKanalix
Monolix
Simulx
R API
Getting Started
Tutorials

Main

Introduction to Monolix

Why should you use Monolix?
The Monolix Workflow
Guide: From NONMEM to Monolix

Data

Data overview

Loading a data set

Data format

Columns used to identify subject-occasions
Columns used to define time
Columns used to define observations
Columns used to define doses
Columns used to define covariates
Columns used to define regressors
Columns used to define controls and events
NONMEM/MonolixSuite differences
Allowed characters

Data formatting

Data formatting presets

Tagging a data set
Specifying observation types and regressor settings
Filtering a data set
Data exploration
How Monolix handles censored data
Import a PKanalix project

Data set examples

Continuous data

Warfarin data set
Theophylline data set
Tobramycin data set
HIV data set
Remifentanil data set
Veralipride data set

Categorical data

Respiratory status data set
Zylkene data set
Inpatient multidimensional psychiatric data set

Count data

Epilepsy attacks data set
Crohn’s Disease Adverse Events data set

Time-to-event data

NCCTG lung cancer data set
Veterans’ Administration Lung Cancer data set
Primary Biliary Cirrhosis data set
Cardiovascular data set
Oropharynx data set

Joint data

PSA and survival data set
Warfarin joint data set
Remifentanil joint data set

Structural model

Setting the structural model
Using or editing models from the libraries
Writing a model from scratch
Mapping between the data and the model

Model syntax: mlxtran language

Introduction: model structure for Monolix

PK: Using PK macros

Introduction
depot macro
pkmodel macro
piecewise macros

EQUATION: Writing model equations

Ordinary differential equations (ODEs)
Delay differential equations (DDEs)

DEFINITION: Defining random variables

Continuous observation model
Time-to-event observation model
Count observation model
Categorical observation model

Language reference

Reserved keywords
If/else statements
Mathematical functions
Operators
Probability distributions

Libraries of models

PK model library
PK/PD model library

TMDD model library

Introduction to TMDD concepts
TMDD library overview

Detailed TMDD models

Full model
Rapid binding (QE) and quasi steady-state (QSS) models
Constant Rtot TMDD model
Irreversible binding TMDD model
Wagner model
Irreversible binding and constant Rtot model
Michaelis-Menten model

Guidelines to choose a TMDD model

Library of PK models with double absorption
Library of parent–metabolite models
Tumor growth inhibition (TGI) library
Time-to-event model library
Count data model library

Typical models

Typical PK models

Single route of administration
Multiple administration routes
Multiple doses and steady state
Mixed first-order and zero-order absorption, or parallel first-order
Saturation of the absorption rate
Absorption delays via sigmoid absorption, transit compartments or Weibull absorption
Models for urine data

Models for non-continuous data

Time-to-event data models
Count data models
Count models with offset
Categorical data models
Joint models for non-continuous outcomes

Typical extensions of structural models

Joint model for continuous outcomes
Mixture of structural models
Using regressors
Time-varying clearance
Auto-induction model
Enterohepatic circulation model
Using a scale factor
Delay differential equations
Outputs and tables
Computing additional model outputs such as Cmax or AUC
Calculating the half-life

Advanced models

Lifespan models
K-PD models
Friberg model for myelosupression
ADC model
Beta regression model

External model editor

Statistical model

Observation model (residual error)

Individual model

Distribution of individual parameters

Understanding shrinkage

Covariate model

Complex covariate-parameter relationships
Time-varying covariates

Inter-occasion variability
Mixture of distributions

Tasks and results

Estimation tasks

Initialization

Initial estimates
Check initial estimates and auto-init

Population parameters

SAEM

The convergence indicator

Bayesian estimation or fixed population parameters

EBEs

Conditional distribution

Conditional distribution task
Statistical tests
Proposal

Standard errors
Likelihood

Convergence assessment

Model building

Automatic covariate model building
Statistical model building with SAMBA

Bootstrap
Output files
Comments
Export a Monolix project

Project comparison in Sycomore

Hierarchy

Table of projects
Selection panel
Tree view

Comparison
Example

Plots

General plot features

Generating and exporting plots
Interacting with the plots
Transferring plot formatting
Export charts data

Data plots

Observed data
Covariate viewer
Bivariate data viewer

Plots for structural model diagnosis

Individual fits
Observations vs predictions
Scatter plot of residuals
Distribution of residuals

Plots for individual model diagnosis

Distribution of individual parameters
Distribution of standardized random effects
Correlation of random effects
Individual parameters vs covariates

Predictive checks

Visual predictive check

Correcting the VPC for missing observations
How Monolix generates the VPC for Time-To-Event data

Numerical predictive check
BLQ predictive check
Prediction distribution

Convergence diagnosis

Convergence of SAEM
Convergence of MCMC
Convergence of importance sampling

Task results

Likelihood contribution
Standard errors of estimates

Reporting

List of reporting keywords
List of plot settings
List of table settings

Submissions and Reproducibility

Submission of Monolix analysis to regulatory agencies
Reproducibility of MonolixSuite results

FAQ

Running through command line

Parallelization over multiple nodes of a cluster

Evaluating MPI Efficiency

Breadcrumbs

Home
Monolix
Plots

On this Page

Convergence diagnosis

The following pages describe the plots related to the convergence diagnosis:

Convergence of SAEM
Convergence of MCMC
Convergence of importance sampling

Pagination

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Prediction distribution
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Convergence of SAEM

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