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 Experiment Videos

Statistical uncertainty in the no-observed-adverse-effect level.

K G Brown1, L S Erdreich

  • 1U.S. Environmental Protection Agency, Environmental Criteria and Assessment Office, Cincinnati, Ohio 45268.

Fundamental and Applied Toxicology : Official Journal of the Society of Toxicology
|August 1, 1989
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Interaction between spent fuel components and carbonate rocks.

The Science of the total environment·2019
Same author

Meta-analysis of stray voltage on dairy cattle.

Journal of dairy science·2009
Same author

Mobile phones, mobile phone base stations and cancer: a review.

International journal of radiation biology·2005
Same author

In vivo ultrasonic measurement of tissue vibration at a stenosis: a case study.

Ultrasound in medicine & biology·2001
Same author

Using human data to protect the public's health.

Regulatory toxicology and pharmacology : RTP·2001
Same author

Radio frequency radiation exposure standards: considerations for harmonization.

Health physics·2001

The no-observed-adverse-effect level (NOAEL) may overestimate safety with small sample sizes due to increased false-negative rates. Statistical power analysis is crucial for accurate risk assessment in toxicology.

Area of Science:

  • Toxicology
  • Statistical Analysis
  • Risk Assessment

Background:

  • The no-observed-adverse-effect level (NOAEL) is a key metric used by regulatory bodies like the U.S. EPA to determine safe exposure levels.
  • Current methods for deriving reference doses (RfDs) from NOAELs may not adequately account for statistical uncertainty, particularly concerning false-negative errors.
  • Decreasing sample sizes in toxicological studies can increase the probability that the true added risk at the NOAEL is not negligibly small.

Purpose of the Study:

  • To address the statistical uncertainty inherent in the NOAEL determination.
  • To highlight the impact of sample size on the reliability of the NOAEL and subsequent risk assessments.
  • To introduce a statistical approach that incorporates the concept of statistical power for more robust risk evaluation.

Related Experiment Videos

Main Methods:

  • Investigated the relationship between sample size, false-positive rates, and false-negative rates in toxicological studies.
  • Introduced the concept of statistical power (the probability of detecting an effect if one exists) into the NOAEL framework.
  • Applied a new statistical procedure to illustrate these concepts using existing literature data for both dichotomous and categorical endpoints.

Main Results:

  • Demonstrated that smaller sample sizes lead to a higher likelihood of false-negative results, potentially underestimating toxicity.
  • Showed that when statistical power is low and no significant effect is observed, the evidence is inconclusive, necessitating further investigation.
  • Illustrated how the expected value of the NOAEL can increase, making an agent appear less toxic with limited data.

Conclusions:

  • The traditional NOAEL approach can be statistically uncertain, especially with small sample sizes, potentially leading to inaccurate risk assessments.
  • Incorporating statistical power analysis provides a more reliable method for evaluating the true added risk at the NOAEL.
  • Inconclusive statistical evidence warrants the collection of additional data or the use of alternative information sources to ensure accurate health risk evaluations.