Skip to content
PMX Solutions
Menu
  • Home
  • Tutorials
    • Abbreviations
    • Visual predictive checks
    • R Related
    • Shiny Application
  • About

Category: VPC Tutorial

VPC Tutorial

A step-by-step guide to prediction corrected visual predictive checks (VPC) of NONMEM models

Introduction Back in 2011, a very nice paper introducing the prediction corrected VPC was published by Bergstrand et al, titled: “Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear Mixed-Effects Models” In …

VPC Tutorial

A step-by-step guide to percentile visual predictive checks (VPC) of NONMEM models

This blog is an extension on the previously discussed scatter VPC. A percentile visual predictive check (VPC) can be used to compare the distribution of the observations with the simulated distributions …

VPC Tutorial

A step-by-step guide to confidence interval visual predictive checks (VPC) of NONMEM models

Last time, we focused on the percentile VPC, which was already quite an improvement to the scatter VPC but one may wonder when looking at a percentile VPC, how certain …

VPC Tutorial

A step-by-step guide to scatter visual predictive checks (VPC) of NONMEM models

There are many different ways to evaluate model performance, and sometimes it seems that there are even more ways to use a visual predictive check (VPC) for your population PK …

Recent posts:

  • Exploring the Capabilities of ChatGPT: A Step-by-Step Guide to Creating a Pharmacokinetic Analysis Shiny App
  • Abbreviations and Terminology Used in Population Pharmacokinetics/Pharmacodynamic Models and in Pharmacometrics
  • Publishing the covariance matrix of population models. Why not?
  • Parallel fast-slow absorption – Modelling the Tortoise AND the Hare
  • Applying MAP Bayes estimation for therapeutic drug monitoring (TDM) in R with mrgsolve
  • Simulating the equi-dosing regimen region in R using mrgsolve – a bottom-up approach
  • How (not) to report pharmacokinetic data
  • Inter-individual and/or inter-occasion variability: what can we quantify in our models and what is the impact on simulations
  • Calculating the power of covariates in population non-linear mixed effects models: the Monte Carlo Mapped Power approach
  • Flawed study design of parent-metabolite pharmacokinetic studies – the prove is in the pee
Copyright © 2023 PMX Solutions – part of Axiaal Data Analytics – Disclaimer – Privacy and cookie statement