Feature of the Week Videos
General
Data
#10 Working with several types of measurements 
: Find out how to match different types of observations to different model outputs, or use only part of your data set.
#12 Modelling different types of drug administration : In Monolix, different types of administrations can be defined in the dataset to be flexibly modelled in different ways.
#17 Taking censored data into account : Monolix automatically uses censored data information for estimation tasks, and simulates BLQ values for efficient graphical diagnosis of the model. All you have to do is tag the censored observations in the data set.
#21 Defining additional doses with columns Steady State and Additional Doses : Multiple doses can be encoded in a compact way in a dataset handled by Monolix. Here is how it works.
#25 Using the “ignored observation” column : Save time by working with a unique data set and selectively tagging columns as “ignored observation”
#51 Encoding data with occasions : Occasions can be useful to define inter-occasion variability, or to use covariates that vary between occasions. There are several possible ways to encode occasions in a dataset to be used in the MonolixSuite. They are shown in this video.
#53 Applying a washout or a selective reset : Do you want to reset your model? Watch this recap of the use of the EVENT ID column and reset() macro.
#89 Missing data : This video shows different ways how Monolix treats datasets with missing information and what are the general guidelines to handle missing data.
#105: Using data set filters (part 1) : From version 2020R1 onward, it is possible to apply filters on your data set to work with a subset of it only. This video shows you how to proceed.
#106: Using data set filters (part 2) : In this video we explain how to combine several filter actions using unions and intersections.
#168 Linear interpolation of regressors in Monolix and Simulx : This video describes two methods for interpolating regressors (last value carried forward and linear interpolation) and when to use each.
#169 Dataset formatting : Learn how to adapt various input data file formats to the standard Monolix and PKanalix format, ensuring quality control and reproducibility with ease. It is a step-by-step guide on utilizing this powerful tool within a user-friendly graphical interface using several examples.
#170: Data formatting presets : This video explains how to save the data formatting steps and easily reapply them on new projects using.
Plots
#01 The beautiful interactive plots of Monolix2018
#03 Stratifying plots in Monolix : Stratifying plots: a simple but impressively powerful new feature of Monolix for data exploration and model diagnosis.
#11 Customizing the plots : Are you looking for publication-ready diagnostic plots? Monolix allows you to change the appearance of the plots to save high-quality figures.
#24 Modifying the plot layout : Find out how to fully customize your plot layout in Monolix.
#60 Id highlighting across plots :Monolix now includes an extended feature to highlight ids across plots! Watch this video to see what a great help it is to diagnose quickly your model.
#78 Creating a custom theme for Monolix plots : You can create one or several custom themes and apply it to all Monolix plots when appropriate, and even share them with your colleagues. It is thus easy to generate ready-to-print figures that meet your personal requirements. From MonolixSutie version 2024R1 use the preset feature.
#100:Trend lines : Can you imagine model building without data visualization? Neither can we, and this is why we keep improving it. Watch this video to see how the new trend lines for observed data work.
#123: Reordering and renaming subplots after a split : This video explains how to order and rename the subplots after splitting the diagnostic plots according to covariate groups.
#143: Choosing the step between tick marks in all plots : In the 2021 version, the possibility to choose the step between tick marks in all plots has been added, such that they better represent the meaning of the axis.
#164: Transfer of plot customizations : Discover how to easily transfer plot customizations between plots in your projects. Watch now to learn this time-saving technique for achieving consistent plot configurations.
#165: Plot presets : Learn how to streamline your workflow by creating and applying plot presets with custom plot configurations across multiple projects.
Software preferences
#22 Preferences of Monolix : Would you like to export automatically all your plots, or keep a trace of all your runs? This kind of advanced features can be enabled in the Preferences.
#55 How to set header preferences : Tired of tagging your data set columns? Learn how to define which columns headers you want to be automatically recognized in this video!
#134 Display settings : Display settings of the MonolixSuite applications offer several options that might resolve display issues or come in useful or practical.
Input and output files
#09 Using the table statement : Learn about how to output variables such as the half-life or AUC from your monolix runs.
#28 When to save, what to save to avoid losing information : Struggling reloading projects? Watch this video to understand the best saving workflow.
#129 Dataset fingerprint : This video explains what is a fingerprint of a dataset and how it is used in Monolix and PKanalix to prevent the invalid results from loading.
#167 Easily Share Projects with Collaborators : This video explains how to easily share projects so that all necessary files are included and the paths are correct. Available in Monolix, PKanalix, and Simulx.
Automated Report Generation
#152 Automated Reporting in Monolix : We explain in details how to generate a full report automatically for a Monolix project.
#171 Automated reporting in PKanalix : Discover the automated reporting feature of PKanalix and learn how to generate a customized report.
#172 How to customize tables in PKanalix reporting : Discover how to create customized tables in PKanalix reporting for internal communication and regulatory-grade documents: select table content, change orientations, split and filter data, rename parameters and covariates, and create custom table styles. This video covers techniques for both general tables and those with occasions, along with display settings, custom table styles and best practices.
Monolix
Structural Model
#02 Using the built-in libraries of models : Discover our greatly enriched model libraries and how to efficiently browse through them.
#14 Writing a structural model with Mlxeditor : In Monolix, it is easy to write a new model or modify a model from the library without mistake thanks to the integrated text editor, with features like syntax highlighting and a compiling check.
#16 Using regression variables in Monolix : This video shows with some examples how regressors can be used in Monolix.
#20 Using the pkmodel() macro to define the PK part of a model : When writing a custom model, the pkmodel macro permits to define all typical PK models in one line of code.
#26 Defining delay differential equations in MonolixSuite : Learn how to easily implement delay differential equations in the MonolixSuite for complex delay based PKPD models such as lifespan models.
#35 Introducing a scale factor to control parameter units : Wondering in which units your estimated parameters are? This video tells you how to change them using scale factors.
#37 Understanding the error messages : Afraid of writing your own model? This video details the meaning of the most typical error m essages encountered when writing a new model.
#38 Computing the AUC within a PK model : Computing the AUC can be done easily within a PK model. Watch this video to see an example of AUC simulation in Mlxplore, Monolix and Simulx.
#39 Good practices for ODE-based models : Watch this video to get all the tricks to be sure that your ODE model will behave as you expect!
#43 Understanding how and when the analytical solution is used : Wondering when the analytical solution of the model is used? This video explains all the details.
#52 Using dose-related keywords in the structural model : The Mlxtran langage includes reserved keywords to use information from the dosing design in the structural model. Learn which ones in this video.
#57 Writing a model for 2 drugs : You want to model two drugs at the same time? Watch this video to see how to define the mlxtran model.
#61 Mapping model outputs to data set observation types : The new mapping panel allows you to precisely map the data set observation ids to the model outputs and leave some out. Discover how in this video.
#75 Focus on the depot macro : This video explains how to use the depot() macro to apply the doses defined in the data set to ODE variables in a model.
#81 Alternative for DDEs : Did you know that many delayed differential equations (DDEs) can be rewritten as ODEs to improve the integration time? This video shows you how, for example on a lifespan model.
#90 Calculating the NADIR or the Cmax in the structural model : The maximum or minimum of any ODE variable can be calculated directly in the structural model. This video shows you how to proceed.
#92: Splitting a dose into several fractions : This short video shows you the different ways of splitting a dose into 2, 3 or more fractions going via different routes. The same principles also apply to other fractions.
#95: Developing a model for two formulations : Do you have a drug in two oral formulations in the same dataset? These formulations can differ in absorption and this video explains in detail how to model it.
#93 Initial integration time : This video shows different initial conditions of a system of ODEs and their impact on the model.
#127: mlxEditor - how to edit mlxtran models : Model editing is as important aspect of modeling and simulation workflows. This video shows how to do it in the mlxEditor - an application of MonolixSuite integrated with Monolix and Simulx and designed for the mlxtran language.
#130 Combining library models : In certain cases, a MonolixSuite user might want to combine different models from the Monolix library. This video explains how to do this by using the example of double absorption PK model combined with a PD model.
#139 Between-subject mixture of structural models : Mixtures of models are useful to represent subpopulations of individuals with different dynamics, for example responders and nonresponders to a given treatment. This video focuses on mixtures of structural models and gives a comparison with mixtures of distributions.
#144 Transit compartments : This video explains the background of a more complex absorption model – absorption through transit compartments. You can watch the video to find out when and how to use transit compartment models in Monolix.
Statistical Model
Random Effects
#06 Shrinkage : Shrinkage is said to bias diagnostic plots but Monolix has a special technique to get around the shrinkage problem. Watch the video to understand how it works.
#31 Inter-individual variability with random effects : Which standard distribution should you choose for a parameter with random effect, and how can you verify that it is appropriate? Find out in this video.
#32 Parameters with no variability : Find out how to remove random effect on a parameter, and how this affects its estimation.
#33 Implementing a custom parameter distribution : Want to use another distribution than those implemented in Monolix? Learn how in this video.
#08 Correlations between random effects : Two clicks are all it takes to define a correlation between random effects in Monolix 2018! As you will see in this video, and more.
#63 Adapted logit-normal distribution : You know that your observations or individual parameters are bounded? Discover how to adapt the logit-normal distribution limits and increase the accuracy of your model.
#64 Inter-occasion variability in Monolix : This video explains how several levels of variability can be combined in Monolix, such as inter-individual variability and inter-occasion variability.
Covariate Effects
#04 Transforming and adding covariates : This video unravels how to add covariates, and transform them to get the desired relationship.
#15 Using time-varying covariates : Time-varying covariates can be used in Monolix, but they have to be defined in the structural model file. This video shows how.
#27 Mixture of distributions with latent covariates : What is a latent covariate? How can it be used in Monolix to model mixtures of distributions for parameters? Find out in this video.
#40 Defining a covariate-dependent standard deviation for a parameter : Find out how to define a parameter with different standard deviations for covariate groups
#62 Calculating the typical value for each category of a categorical covariate : Are you perplexed by the beta parameters estimated by Monolix? This video shows you how to calculate the typical value for each category of a covariate.
#70 Scaling of continuous covariates : Covariates are used to explain intra-individual variability of population parameters, but they can lower the confidence in the parameter estimation. Watch this video to see that a correct covariate scaling can prevent it.
#135 Discretizing a continuous covariate into a categorical covariate : Continuous covariates can be discretized into categorical covariates using the Monolix covariate transformation panel in the GUI. This procedure allows to keep until all convenient covariate tools such as statistical tests.
#141: How to replace missing covariate values in Monolix : This video shows how to encode missing continuous covariate values in your data and replace them by an imputed value in the Monolix interface.
Residual Error
#05 Residual error models : How to diagnose and choose the best residual error model for continuous data in Monolix.
#34 Using different error models for different studies : Find out how to model data from different studies with different error models while keeping the same population model.
Model Selection Tools
#79 Understanding and using the statistical tests in Monolix : The statistical tests complement the diagnostic plots to guide the user in the development of the statistical model. This video explains what they mean and how to use them.
#80 Proposal : Monolix is able to use the individual parameters of the current run to pre-test several covariate, correlation and error models. The most promising statistical model is displayed in the “proposal” section and can be applied in a single click, before running it in the population framework.
#149 Likelihood ratio test : Automatic model building strategies in Monolix use the likelihood ratio test to compare different models. This video explains what the likelihood ratio test is and when and how it can be used.
Monolix Tasks and Workflow
#07 Initial estimates : Choosing relevant initial estimates is crucial to get a fast convergence. Discover the features that guide this step in Monolix2018.
#18 Tasks of the Monolix workflow : This video explains how the different estimation and diagnosis tasks in Monolix are used together in a workflow, and what their results are.
#19 Using the “use last estimates” button : To speed up the convergence of a run, the initial parameter values can be read from the estimates of the previous run. Learn how and when to use it.
#29 Exporting the plots data to replot elsewhere : Learn how to export the plots data from Monolix to reuse them with your other favorite software.
#42 Convergence assessment : Find out how easy it is to evaluate the convergence on multiple replicates.
#56 Reference in check initial estimates : Discover the new reference curve feature in the “check initial estimates” of Monolix in this video!
#58 Automatic initialization of parameters : Setting up your PK model is now even faster, thanks to the new automatic initialization of parameters for models from the PK library.
#69 Sycomore : We know that development of a good model is a long road of incremental improvements, which looks like a branched tree with different ideas and strategies. Sycomore is a visual and interactive tool designed to manage efficiently your Monolix runs and compare them side-by-side.
#74 Files necessary to share a Monolix run or submit to the regulatory agencies : Want to include a Monolix run into a submission to regulatory agencies or share a run with somebody else? This video explains the files to include.
#76 Calculate EBEs for a new data set using an existing model : You estimated a model on one data set and you want to use it for individual fits in another, for example sparse, data set? This video explains how to skip the re-estimation of population parameters when you load new data.
#83 Baseline PD models : The baseline of PD models can be estimated as a model parameter, fixed to the observed value or a mixture of both. This video shows you how to implement these three options.
#85 Modeling exposure-response curves : This video shows how to use exposure-response models to obtain exposure-response curves directly in Monolix.
#94: Individual parameters : This video shows how individual parameters are defined, estimated and where they are used in Monolix.
#122: Generalized auto – initialization of parameters : This video describes the improvements done to the auto-init – a smart feature to initialize population parameters. There are some examples using models from the Monolix libraries and custom models, and it shows good practices in case of complex models.
#156: Importing PK parameters from a previous analysis with data formatting : In a typical sequential modeling approach, PK parameters are estimated with a first PK dataset, and in a second step, they are used for modeling PD data with a joint PKPD model. This video shows how to do this directly in the interface without modifying the original PD dataset -using the data formatting module.
#162: Bootstrap : This video shows how to run bootstrap in the Monolix interface -an alternative approach to estimate confidence of population parameters. It explains settings to customize the sampling and estimation process, and automatically generated results and plots.
#175 Allometric scaling using fixed exponents : A common approach to allometric scaling is to use a power law relationship between the parameters and the typical body weight of the species. This video shows how to do it in MonolixSuite by adding the body weight as covariate in the model developed on the pre-clinical data with either fixed or estimated coefficient.
Algorithms
#44 Understanding how simulated annealing is used for parameter estimation : This video explains what is simulated annealing, an advanced setting of the parameter estimation algorithm, and in which case it may be disabled.
#45: Understanding how SAEM works : Frustrated to use Monolix as a black box? This video explains what is exactly going on during the population parameters estimation.
#46 A few useful SAEM settings : Not sure what the settings exactly mean? Discover the main ones in this video.
#47 The convergence indicator : This video explains what the convergence indicator exactly is and how to use it.
#49 Estimation methods and Bayesian approach : Bayesian estimation allows to take into account prior information for the estimation of parameters. Find out with this video how to use it in Monolix.
#86 Estimating the conditional distributions : This video explains how the Monolix task “Conditional distribution” is computed and what is shown in the graphical report.
#111: Demystifying probability distributions in Monolix : Several probability distributions are used in the Monolix algorithms. This video explains the meaning of each distribution and its closed form solution, when it exists.
#113: How the EBEs are calculated : In this video, we explain the meaning of the EBEs and how they are calculated in Monolix.
#145 Estimating the Standard errors using Stochastic Approximation : Do you wonder what is behind the Stochastic Approximation method to estimate standard errors in Monolix? This video will tell you all about it.
#147 Calculating the likelihood via importance sampling : This video explains what the likelihood is, why it requires a separate task and how it is calculated via importance sampling.
#150 Linearization method: standard errors and likelihood : This video explains the linearization method to calculate standard errors and likelihood.
Results
#23 Interpreting the correlation matrix of the estimates : The correlation matrix can help detect a model over parametrization. Discover how in this video!
#41 Calculating the coefficient of variation : The coefficient of variation can easily be calculated based on the Monolix outputs. This video shows you how.
Diagnostic Plots
#48 What is the VPC, and how to get the most out of it : This video explains how the VPC is built and how to modify it in Monolix to get the most informative plot.
#66 Interpreting the PD versus PK plot – the example of hysteresis : The observation of the PK and PD data in Datxplore can bring useful insights to choose an appropriate model. Learn how to detect hysteresis in this video.
#72 Display and informativeness of the BLQ data in the plots : To display BLQ data in the plot, Monolix uses simulated BLQ values. This video explains how these values are generated and why they improve the diagnostic power of the plots.
#73 Typical patterns in the Obs versus Pred plot : Puzzled by the deviation you see in your Obs versus Pred plot? Learn how to interpret the most typical patterns!
#77 Generating predictive checks on an external data set : We show how to generate predictive checks, such as an external VPC, to check whether a population model estimated on a single dose study is also valid on a new multiple dose study for the same molecule.
#133 VPC with time after the last dose : A new feature of Monolix2021 allows to re-calculate the VPC plot with times after the last dose. It can make a more informative plot to diagnose the model and identify misspecifications.
#142 Customizing time grid in the Individual fits plot : In this video, we show how to change intervals between predictions on the Monolix Individual fits plot, making the plots more or less smooth and improving model diagnosis.
#160: Creating forest plots : Forest plots are powerful tools that can be used to visually represent the impact of covariates on exposure parameters. Watch closely as we navigate through the intricacies of these plots, transforming data into clear insights using applications of the MonolixSuite.
Modeling Non-Continuous Data
#13 Encoding and representation of TTE data : Learn how to encode different types of time-to-event data for flexible modelling in Monolix.
#50 Encoding and visualization of count and categorical data : Monolix also handles count and categorical data! Learn how to encode this type of data and how to explore it in Datxplore.
#65 Time-to-event modeling with Monolix : Powerful modeling of time-to-event data with a parametric approach can be performed in Monolix, and is facilitated by a TTE models library. Discover how to do it in practice with this video.
#82 Kaplan-Meier estimator : Survival function is a key function in the analysis of time-to-event data. In general, it is unknown and a typical way to estimate it is through the non-parametric Kaplan-Meier estimator. This video explains step by step how to construct it for exact and censored events and shows useful visualization features.
#84 VPC for time-to-event data : VPC of survival data is a necessary diagnostic plot in the modeling of time-to-event data. This video explains how Monolix generates it for exact and interval censored events and presents settings that increase the modeling accuracy.
#140 Modeling categorical data 1 : Responders/non-responders, pain levels or answers to a questionnaire. All are categorical that can be modeled in Monolix. This video presents typical models for binary, nominal and ordinal categories and explains how to implement them in the mlxtran language.
#146 Modeling categorical data 2 : This video shows an example of categorical modeling of pain levels. It explains, from a practical point, how to build a structural model, assess the goodness of fit and how to interpret the results. Presented methods can be applied to other categorical data case studies.
Simulx
Definition
#98:Importing a Monolix project to Simulx : Get started in Simulx by importing your favorite Monolix project. This video will show you how Simulx defines elements based on a monolix project to help you simulate new designs.
#99:Defining a new treatment in Simulx : Simulx offers flexible options to define and simulate new dosing regimens. Discover them in this video!
#104: Simulation outputs : This video shows the different options to define simulation outputs.
#108: Scaling a dose amount by a covariate in Simulx :This week, we have a deeper look at the option to scale dose amounts by covariates in Simulx. Why is this useful? How to choose the correct scaling? Here we provide three examples to help you get started with personalized treatments.
#112: Additional lines in the model in Simulx : When you need a simulation output with a variable that is not defined in the Mlxtran model, then use the “additional lines in the model” feature to add new variables without modifying the original file. Watch this video for more details.
#121: Defining treatment cycles with the “repeat” option in Simulx : Intermittent dosing with periods on treatment and periods off are useful to mitigate toxicities. In Simulx, when defining a new treatment element, you can use the “repeat” option to repeat a base pattern several times.
#124: Using the uncertainty of the population parameters in Simulx : This video explains how to take into account the uncertainty of the population parameters after importing a Monolix project.
#154 Import a PKanalix project to Simulx : Import your compartmental analysis run from PKanalix to Simulx
Exploration and Simulation
#30 Exploring a TMDD model with Mlxplore : The application Mlxplore of the MonolixSuite can help you identify the impact of some parameter on your model. This video shows how this is done with a TMDD model.
#36 Exploring new dosing regimens in Mlxplore : Quick simulations of your model estimated with Monolix can be computed with Mlpxlore, for example to explore new dosing regimens.
#97: Simulx interface : MonolixSuite2020 is now released! Here is a quick look at its main improvement: a new interface for Simulx, our applications for clinical trial simulations, and the new dark theme for MonolixSuite interface.
#101: Exploration in Simulx : With the exploration tab of Simulx, you can interactively explore new treatment designs and parameter values, and check the result on a typical prediction. Discover how with this video.
#103: Simulation groups : Make the most of Simulx by using simulation groups! In this video, check how to simulate clinical trials with several arms.
#114: Using same individuals among groups in Simulx : When several simulation groups are simulated in Simulx, you have the choice to use same individuals among the groups. Check with this video how this feature works and when it is relevant.
#115: Methods to sample from tables in Simulx : This video explains the different sampling methods available in Simulx for a simulation using an external table or tables created after an import from Monolix.
#120: Sampling shared ids from external tables in Simulx : When Simulx samples individual values from several external tables, the option “shared ids” can be used to choose between independent and common samplings. Watch this video to see how it works and when it is useful.
#128: Sampling from the population parameter uncertainty in Simulx: behind the scene : This video explains the procedure Simulx uses to sample population parameters from their uncertainty distribution.
#131: Applying uncertainty of the population parameters on the fixed effects only : This video shows how to take into account the uncertainty on the fixed effects but ignore the random effects in Simulx.
#157 Calculating outcomes in Simulx : This video describes post-processing of simulations outputs into outcomes: how to create them and what they consists of.
#158 Calculating endpoints in Simulx : This video describes how to summarize outcomes into endpoints for each simulation group.
#159: Group Comparison of Endpoints in Simulx : This video explains group comparison of endpoints in Simulx for clinical trial simulations. It also includes an example with lixoftConnectors to automate selection of optimal sample size.
Results and Plots
#107: Easy sharing of your Simulx project : Have you noticed the option “save the user files in the results folder” in the Simulx project settings? This copies all the external files used in your project with the results, which is especially useful to move your simulations or share them with a colleague. Watch this video to know more!
#126: Exporting simulated data as MonolixSuite formatted dataset : This video describes how to use the Export simulated data feature in Simulx and exportSimulatedData function in lixoftConnectors R package using an example of bioequivalence study design.
#137 Bootstrap: visualization of uncertainty : Although parameter uncertainty can be visualized in Simulx using elements automatically created after importing a Monolix project, bootstrap estimates can be used to visualize uncertainty as well by saving estimates to a file and creating a population parameter element in Simulx that reads the parameters from this file.
#138 Overlay Data on your Predictions : It is now possible to overlay data on your predictions in Simulx! Whether you are still exploring your data, whether you already have a good model or whether you are in the validation phase, we show you in this video how Simulx can guide you towards the next step with its new feature in the exploration tab.
#151 Visual Cues in Simulx : Keep in mind your ultimate goal with visual cues in Simulx. Draw your targets on the result plots and get a first estimate of relevant endpoints.
#153 Merging output distributions in Simulx : Merge your output distributions on the same plot to compare groups.
#155 Original IDs from input to output tables in Simulx : When and where original ids appearing in the input table elements are shown and saved in the result tables
PKanalix
Data and Settings
#54 Check lambda_z regression in PKanalix : For this first feature of the week on PKanalix, discover the “check lambda_z” tab, very useful to visualize and control the calculation of the terminal elimination phase λz.
#102: Units in PKanalix : Now it is possible to manage and display NCA and CA results in your preferred metric units. It gives flexibility in reporting and makes the analysis more realistic. This video explains how to do it.
Calculation Methods and Workflow
#59 Quick compartmental analysis with PKanalix : In addition to NCA, one of the main features of PKanalix is a quick calculation of PK parameters in the Compartmental Analysis framework.
#109: Selecting NCA parameters in PKanalix : The settings for non-compartmental analysis in PKanalix now include a convenient way to select a list of NCA parameters to compute and display in the results. The default list can also be customized.
#116: PKanalix behind the scenes: integral methods for AUC : This video, the first in a series dedicated to computation methods used in PKanalix, explains integral methods in the NCA task settings to compute AUC, AUMC and interpolation for partial AUC.
#117: PKanalix behind the scenes: acceptance criteria : This video explains the NCA acceptance criteria used to flag profiles that satisfy specific conditions for the adjusted R2, for the percentage extrapolated AUC and for the SPAN.
#118: PKanalix behind the scenes: lambda-Z : Lambda-z parameter corresponds to the slope of the terminal elimination phase. This video explains how linear regression, residuals and adjusted R2 coefficient are used to calculate it.
#119: PKanalix behind the scenes: weighting : This video explains why we use the weighted objective function in the compartmental analysis in PKanalix and how different weights can account for data points with constant or proportional measurement noise.
#163: Custom NCA parameters : This video explains how to create user-defined NCA parameters.
#166 Calculating an absolute bioavailability with PKanalix : If you data over intravenous and extravascular doses, you can use PKanalix to calculate the absolute bioavailability. This video shows how to proceed in case of a parallel design using the Bioequivalence module, and in case of a crossover design using the Ratio module.
#173 Sparse data : This video explains how to perform an NCA analysis on sparse data, that is, where there is only one or very few observations per individual (or occasion). It covers all you need to know to perform the analysis, including stratifying the data to calculate mean profiles and options to handle BLQ samples.
Results and Plots
#67 Graphical results of NCA in PKanalix : Automatic plots are part of the efficient NCA workflow of PKanalix. Discover how to interpret and customize them!
#110: Splitting the summary table in NCA results : In this video, discover a new feature of PKanalix that allows to split the non-compartmental analysis summary between different groups of individuals.
#132 Plotting the concentration profiles split by individual in PKanalix : This video shows how to plot pharmacokinetic profiles split by individual in PKanalix using the novel Individual fits plot.
#174 Table of concentrations in PKanalix : This video introduces the new Table of Concentrations feature in PKanalix 2024. After performing Non-Compartmental Analysis, the table summarizes concentration data and allows viewing individual concentrations or stratifying them by covariates, for flexible data review.
R
#68 Scripting Monolix in R : Sometimes you might want to use Monolix not through the interface but with scripts, for example to automate a set of actions in Monolix. The MonolixSuite comes with an API for R that allows to use Monolix and PKanalix from R, such that all you can do with the interface can be done with the API. Some examples are shown in this video.
#71 Scripting PKanalix in R : Like Monolix, PKanalix has an API for R that can be used to easily automate non-compartmental and compartmental analyses and post-process the results. Watch some examples in this video!
#148 Scripting Simulx in R : This is a concrete example to learn using Simulx API for R. We import a Monolix project and simulate a new output variable with the EBEs. Example R script used in this video
#125 Generating the diagnostic plots in R with lixoftConnectors : The lixoftConnectors R package, the MonolixSuite API for R, allows to script the MonolixSuite. Starting with the 2021 version of MonolixSuite, the plots of Monolix and PKanalix are available also as ggplot objects in R, thanks to new functions in the package lixoftConnectors.
#136 Bootstrap : Bootstrapping is available for the Monolix projects through the bootmlx function of the Rsmlx R package.
#161: Forest plots: automation using lixoftConnectors : Examples of forest plots that account for between-subject variability and automation of the process using the lixoftConnectors R package.