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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
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Monolix
Tasks and results
Estimation tasks
On this Page
Population parameters
SAEM
Bayesian estimation or fixed population parameters
