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

Qualifying ELISA data: combining information.

J J Liao1, J W Lewis

  • 1Genetics Institute, Andover, Massachusetts 01810, USA.

Journal of Biopharmaceutical Statistics
|December 5, 2000
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

Current status of infectious diseases among migrants and non-citizens in Malaysia.

Tropical biomedicine·2023
Same author

Helminth parasites of the wood mouse <i>Apodemus sylvaticus</i> in Southern England: levels of infection, species richness and interactions between species.

Journal of helminthology·2023
Same author

A century of the <i>Journal of Helminthology</i>.

Journal of helminthology·2023
Same author

Public health status of Myanmar refugees in South East Asia: A Malaysian case study.

Tropical biomedicine·2022
Same author

Spatial interactions between two nematode species along the intestine of the wood mouse <i>Apodemus sylvaticus</i> from woodland and grassland sites in southern England.

Journal of helminthology·2021
Same author

Post-era mass drug administration: an update on intestinal parasitic infections in urban poor communities in Peninsular Malaysia.

Tropical biomedicine·2021
Same journal

Correction.

Journal of biopharmaceutical statistics·2026
Same journal

Leveraging external controls in clinical trials: estimands, estimation, assumptions.

Journal of biopharmaceutical statistics·2026
Same journal

Special issue of nonclinical statistics in regulatory applications guest editors' notes.

Journal of biopharmaceutical statistics·2026
Same journal

Comparison of flexible parametric modeling and nonparametric methods to estimate restricted mean survival time: A simulation study.

Journal of biopharmaceutical statistics·2026
Same journal

Simulated treatment comparisons with jackknife pseudo values for estimating population-adjusted marginal treatment effects.

Journal of biopharmaceutical statistics·2026
Same journal

Sample sizes for randomized controlled trials utilizing Bayesian response adaptive randomization for continuous outcomes.

Journal of biopharmaceutical statistics·2026
See all related articles

Assay controls should have minimal variability. This study introduces a mixed-effect model to improve enzyme-linked immunosorbent assay (ELISA) data quality by accounting for inter-assay variation in assay standards.

Area of Science:

  • Biochemistry
  • Analytical Chemistry
  • Immunology

Background:

  • Immunoassay and bioassay data often suffer from significant inter-assay variability in assay standards.
  • Assay controls are crucial for monitoring performance and setting acceptance criteria, ideally exhibiting minimal inter-assay variability.

Purpose of the Study:

  • To develop a novel mixed-effect calibration model for assay controls.
  • To establish new acceptance criteria and improve the qualification of enzyme-linked immunosorbent assay (ELISA) data.
  • To address limitations of traditional fixed-effect calibration models.

Main Methods:

  • Development of a mixed-effect calibration model specifically for assay controls.
  • Incorporation of inter-assay variation from assay standards into the model.

Related Experiment Videos

  • Consideration of the inherent nature of assay controls within the modeling framework.

    • Main Results:

    • The proposed mixed-effect model effectively handles inter-assay variation in assay standards.
    • The model provides a more robust method for setting acceptance criteria for assay controls.
    • Improved qualification of ELISA data is achieved by accounting for control variability.

    Conclusions:

    • The mixed-effect calibration model offers a superior approach to managing assay control variability compared to fixed-effect models.
    • This method enhances the reliability and accuracy of immunoassay and bioassay data.
    • The developed model provides a foundation for more stringent and reliable assay validation processes.