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IntroBayesNONMEM

Introduction to Bayesian pharmacometric data analysis using NONMEM

This repo contains the developing content for an introductory workshop on Bayesian pharmacometric data analysis using NONMEM.

Objectives

  • Introduce principles and methods of Bayesian data analysis for pharmacometric applications.
  • Provide a hands-on experience in Bayesian data analysis using NONMEM.

Primary intended audience: Pharmacometricians

Background assumed

  • Population PKPD modeling
  • Use of R and NONMEM

Workshop outline

  • Why Bayesian?
  • Introduction to Bayesian statistical principles and methods
    • Bayes Rule
    • Bayesian modeling & inference process
  • Computation for Bayesian modeling
    • Maximum a Posteriori (MAP) Bayes
      • Individual: NONMEM POSTHOC
      • Population: Penalized Maximum Likelihood
    • Full Bayesian analysis
      • General computational approach: posterior simulation
      • Brief intro to Markov chain Monte Carlo (MCMC) simulation
        • Gibbs sampling
        • Metropolis-Hastings
        • Hamiltonian Monte Carlo and NUTS
  • Overview of NONMEM implementations
    • MAP estimation
      • Using prior distributions with optimization methods
    • MCMC: BAYES and NUTS methods
    • Prior specification in NONMEM
  • Hands-on 1: Example illustrating Bayesian data analysis workflow
  • Prior distributions
    • Role of a prior distribution
    • Informative, uninformative or weakly informative?
  • Hands-on 2: MAP popPK with selective use of informative priors for nuisance parameters: pediatric atorvastatin
  • Model evaluation and comparison
  • Assessing convergence and choosing numbers of burn-in and post-burn-in samples
  • Getting your hands on posterior samples for individual parameters and predictions
  • Hands-on 3: Full Bayes popPK with selective use of informative priors for nuisance parameters: pediatric atorvastatin
  • When stuff goes wrong
    • Diagnosing and remedying sampling problems encountered with MCMC
    • Reparameterization, e.g., centered vs non-centered parameterizations for hierarchical models
    • Prior distributions as part of the solution
  • Hands-on 4: Full Bayes popPKPD using semi-mechanistic model
    • Friberg-Karlsson semi-mechanistic model for drug-induced myelosuppression
    • Informative priors for drug-independent system parameters
  • Practical strategies for selecting Bayesian estimation methods for specific types of problems
    • When to go Bayes (and why)?
    • Which method?
    • Which tool?
  • Preview of Bayesian data analysis using Stan and Torsten
    • Brief intro with demo
    • Advantages/disadvantages
  • What didn't we cover?