Simulx User Guide – version 3.3
User Guide of mlxR 3.1 published with bookdown is available: HTML – pdf
Download the R scripts and Mlxtran model files used for this user guide
Introduction
Calculation of functions of time
- Function of time with analytical expression
- Function of time solution of a system of ordinary differential equations (ODEs)
- Function of time solution of a system of delay differential equations (DDEs)
- Model with regression variables
Simulation of dynamical systems – PK models
- ODE based system with source terms
- Simulation of PK models: single route of administration
- Simulation of PK models: multiple routes of administration
- Simulation of PK models: weight-based dosing
- Simulation of PK models: simulating nonadherence
Simulation of longitudinal data
- Continuous data
- Categorical data
- Categorical data with Markovian dependence
- Count data
- Time to event data
- Joint model
- Censored data (below or above a limit of quantification)
Simulation of hierarchical models
- Introduction
- Model with random individual parameters
- Model with continuous random covariates
- Model with categorical random covariates
- Model with random population parameters
Extensions of the statistical model
- Library of probability distributions
- Correlation between variables
- Inter occasion variability (mlxR >= 3.2.2)
Simulation of several individuals and replicates
- Defining groups: part I
- Defining groups: part II
- Simulating several replicates of the same experiment
- Computing summary statistics
- Reading data files and using data frames
Using Simulx with a Monolix project
- Using and modifying the original design
- Resampling the patients of the original study
- Introducing uncertainty on the population parameters
Miscellaneous
- Using a model defined “inline”
- Creating outputs not defined in the model (version >=3.1.1)
- Writing the results to a file or a folder
- Reproducing the same random results
- Optimizing the computation time
- Simulating random variables in a block EQUATION
- Using a R model instead of Mlxtran
- Using a PharmML model instead of Mlxtran