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 Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the 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...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Toxicity Testing in Animals01:23

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...

You might also read

Related Articles

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

Sort by
Same author

Visualizing the NIOSH Pocket Guide: Open-source web application for accessing and exploring the NIOSH Pocket Guide to Chemical Hazards.

Journal of occupational and environmental hygiene·2023
Same author

Impact of Organism Allocations on Potency Estimates from Ceriodaphnia dubia Reproduction Tests.

Environmental toxicology and chemistry·2023
Same author

ToxicR: A computational platform in R for computational toxicology and dose-response analyses.

Computational toxicology (Amsterdam, Netherlands)·2023
Same author

Identifying sensitive windows of airborne lead exposure associated with behavioral outcomes at age 12.

Environmental epidemiology (Philadelphia, Pa.)·2021
Same author

Phase 3 adaptive trial design options in treatment of complicated urinary tract infection.

Pharmaceutical statistics·2018
Same author

On the impact of sample size on median lethal concentration estimation in acute fish toxicity testing: Is n = 7/group enough?

Environmental toxicology and chemistry·2018

Related Experiment Video

Updated: Jun 9, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Comparing methods for analyzing overdispersed count data in aquatic toxicology.

Douglas A Noe1, A John Bailer, Robert B Noble

  • 1Department of Statistics, Miami University, Oxford, Ohio 45056, USA. noeda@muohio.edu

Environmental Toxicology and Chemistry
|September 8, 2010
PubMed
Summary

Statistical methods for aquatic toxicity count data can be inaccurate if overdispersion is present. Quasi-likelihood (QL) and generalized linear mixed models (GLMM) offer more robust analysis for count endpoints, especially in aquatic toxicity testing.

More Related Videos

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies
09:37

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies

Published on: January 19, 2022

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

Related Experiment Videos

Last Updated: Jun 9, 2026

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies
09:37

Topical Application Bioassay to Quantify Insecticide Toxicity for Mosquitoes and Fruit Flies

Published on: January 19, 2022

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox
05:47

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Published on: August 28, 2019

Area of Science:

  • Environmental toxicology
  • Statistical modeling
  • Ecotoxicology

Background:

  • Aquatic toxicity tests utilize various measurement scales, including count data (e.g., number of offspring).
  • Standard analysis often assumes a Poisson distribution for count data, which may be violated by extraneous variability (overdispersion).
  • Incorrect distribution assumptions can lead to erroneous statistical inference in toxicity assessments.

Purpose of the Study:

  • To investigate the impact of overdispersion and outliers on the statistical analysis of count data in aquatic toxicity studies.
  • To compare the performance of different statistical methods under various count data scenarios.
  • To identify robust methods for analyzing count endpoints in ecotoxicology.

Main Methods:

  • A computer simulation study was conducted to assess count data analysis methods.
  • Methods compared included those assuming Poisson, negative binomial, quasi-likelihood (QL), and generalized linear mixed models (GLMM) distributions.
  • Simulations evaluated performance under conditions of true Poisson, overdispersed counts, and potential outliers.

Main Results:

  • Poisson-assumed methods produced inflated Type I error rates and underestimated standard errors when count data were overdispersed.
  • Negative binomial methods performed best when data were truly negative binomial but lacked robustness in other scenarios.
  • Quasi-likelihood (QL) and GLMM methods demonstrated reasonable performance across tested conditions, with QL being preferable due to fewer assumptions.

Conclusions:

  • Overdispersion in aquatic toxicity count data significantly impacts statistical inference when using standard Poisson models.
  • Quasi-likelihood (QL) and generalized linear mixed models (GLMM) provide more reliable analysis for overdispersed count data.
  • Quasi-likelihood (QL) is recommended for its balance of performance and fewer assumptions in analyzing count endpoints in ecotoxicology.