Related Experiment Video
Updated: Jul 19, 2026

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
Published on: December 10, 2012
Performance of an iterative two-stage bayesian technique for population pharmacokinetic analysis of rich data sets
Johannes H Proost1, Douglas J Eleveld
1Research Group for Experimental Anesthesiology and Clinical Pharmacology, Department of Anesthesiology, University Medical Center Groningen, University of Groningen, PO Box 30001, 9700 RB Groningen, The Netherlands. j.h.proost@rug.nl
Purpose:
To test the suitability of an Iterative Two-Stage Bayesian (ITSB) technique for population pharmacokinetic analysis of rich data sets, and to compare ITSB with Standard Two-Stage (STS) analysis and nonlinear Mixed Effect Modeling (MEM).
Materials And Methods:
Data from a clinical study with rapacuronium and data generated by Monte Carlo simulation were analyzed by an ITSB technique described in literature, with some modifications, by STS, and by MEM (using NONMEM). The results were evaluated by comparing the mean error (accuracy) and root mean squared error (precision) of the estimated parameter values, their interindividual standard deviation, correlation coefficients, and residual standard deviation. In addition, the influence of initial estimates, number of subjects, number of measurements, and level of residual error on the performance of ITSB were investigated.
Results:
ITSB yielded best results, and provided precise and virtually unbiased estimates of the population parameter means, interindividual variability, and residual standard deviation. The accuracy and precision of STS was poor, whereas ITSB performed better than MEM.
Conclusions:
ITSB is a suitable technique for population pharmacokinetic analysis of rich data sets, and in the presented data set it is superior to STS and MEM.
More Related Videos
08:03Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
Published on: December 7, 2021
08:43PTR-ToF-MS Coupled with an Automated Sampling System and Tailored Data Analysis for Food Studies: Bioprocess Monitoring, Screening and Nose-space Analysis
Published on: May 11, 2017
Related Concept Videos
Analysis of Population Pharmacokinetic Data
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Model Approaches for Pharmacokinetic Data: Compartment Models
Two primary types of compartment models are recognized: mammillary and catenary. The more...