Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

199
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
199
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

172
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
172
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

870
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
870
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

125
Body:Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to...
125
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

188
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and 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...
188
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

346
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
346

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Optimal designs for discrete-time survival models with competing risks.

Lifetime data analysis·2026
Same author

Reflective-type guided-mode resonance sensor system based on interference line fringe shifting.

Optics express·2025
Same author

[Development of dynamic multi-time-point clinical prediction models for bronchopulmonary dysplasia in preterm infants with gestational age < 32 weeks].

Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics·2025
Same author

Minimax optimal designs via particle swarm optimization methods.

Statistics and computing·2025
Same author

Nature-inspired metaheuristics for optimizing dose-finding and computationally challenging clinical trial designs.

Clinical trials (London, England)·2025
Same author

Design optimization of longitudinal studies using metaheuristics: Application to lithium pharmacokinetics.

Statistical methods in medical research·2025

Related Experiment Video

Updated: Dec 6, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Hybrid algorithms for generating optimal designs for discriminating multiple nonlinear models under various error

Ray-Bing Chen1,2, Ping-Yang Chen1, Cheng-Lin Hsu1

  • 1Department of Statistics, National Cheng Kung University, Tainan, Taiwan.

Plos One
|October 5, 2020
PubMed
Summary

This study introduces hybrid algorithms using particle swarm optimization (PSO) to find optimal experimental designs. These new methods efficiently discriminate between scientific models, outperforming existing approaches.

More Related Videos

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.4K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K

Related Experiment Videos

Last Updated: Dec 6, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

17.4K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.8K

Area of Science:

  • Statistics
  • Experimental Design
  • Computational Science

Background:

  • Optimal experimental design for discriminating between scientific models is challenging due to non-differentiable criteria and nested optimization.
  • Existing algorithms are often slow, model-specific, or criterion-specific, limiting their applicability.

Purpose of the Study:

  • To develop novel hybrid algorithms based on particle swarm optimization (PSO) for model-based optimal experimental design.
  • To address complex optimization problems, including singular designs, underspecified models, and deep nested optimizations (up to 4 layers).
  • To demonstrate the generalizability and efficiency of the proposed algorithms across various models and criteria.

Main Methods:

  • Hybrid algorithms integrating particle swarm optimization (PSO) for solving complex, multi-layered optimization problems in experimental design.
  • Application of PSO-based algorithms to classical examples and a toxicological dose-response study with five competing models.
  • Development of an R package for generating discriminating designs and evaluating design efficiencies.

Main Results:

  • The proposed PSO-based algorithms effectively find optimal or highly efficient discriminating designs.
  • These algorithms demonstrate speed advantages over traditional methods and are not limited to specific models or criteria.
  • Successful application to a toxicological dose-response study, comparing performance against existing algorithms.

Conclusions:

  • Hybrid PSO algorithms provide a robust and efficient solution for model-based optimal experimental design, particularly for discriminating between competing models.
  • The developed algorithms offer a more general and faster alternative to existing methods.
  • The accompanying R package facilitates the practical implementation of informed experimental design choices.