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

A method to quantify deviations from assay linearity

J S Krouwer1, B Schlain

  • 1Ciba Corning Diagnostics Corp., Medfield, MA 02052.

Clinical Chemistry
|August 1, 1993
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

How to improve total error modeling by accounting for error sources beyond imprecision and bias.

Clinical chemistry·2001
Same author

Testing claimed minimal detectable concentrations of in vitro medical diagnostic devices.

Clinical chemistry·2000
Same author

A stochastic approximation method for assigning values to calibrators.

Clinical chemistry·1998
Same author

Two-stage procedure for evaluating interassay carryover on random-access instruments.

Clinical chemistry·1996
Same author

A simple, graphical method to evaluate laboratory assays.

European journal of clinical chemistry and clinical biochemistry : journal of the Forum of European Clinical Chemistry Societies·1995
Same author

Modification of NCCLS EP10 to include interference screening.

Clinical chemistry·1995
Same journal

Patterns of One-Year Change in HbA1c and Continuous Glucose Monitoring (CGM) Metrics in Older Adults with Type 2 Diabetes.

Clinical chemistry·2026
Same journal

TSH Pediatric Reference Intervals: Lack of CALIPER Applicability to US-Based Populations.

Clinical chemistry·2026
Same journal

Rapid Detection of Hemoglobinopathy Variants Using One-Step Library Preparation and Nanopore Sequencing.

Clinical chemistry·2026
Same journal

Editor's Note: Circulating Proteolytic Products of Carboxypeptidase N for Early Detection of Breast Cancer.

Clinical chemistry·2026
Same journal

In Reply to Reflexing NT-proBNP for sFlt-1/PlGF Ratios That Fall into the Measurement Uncertainty for Preeclampsia Risk Classification.

Clinical chemistry·2026
Same journal

Reflexing NT-proBNP for sFlt-1/PlGF Ratios That Fall into the Measurement Uncertainty for Preeclampsia Risk Classification.

Clinical chemistry·2026
See all related articles

This study introduces a statistical method to measure assay bias from nonlinearity, providing a confidence interval for deviation magnitude. This goes beyond simply detecting nonlinearity, offering crucial quantitative insights for assay validation.

Area of Science:

  • Analytical Chemistry
  • Biostatistics
  • Assay Development

Background:

  • Assay linearity is critical for accurate quantitative results.
  • Existing methods often only detect nonlinearity, not quantify its impact.
  • Nonlinear assay responses can introduce significant bias.

Purpose of the Study:

  • To develop a statistical method for quantifying deviations from linearity in assays.
  • To provide a least-squares estimate with a confidence interval for assay bias due to nonlinearity.
  • To offer a method that surpasses the limitations of traditional lack-of-fit tests.

Main Methods:

  • A statistical procedure designed for unequally spaced analyte levels and nonconstant variance.
  • Involves adding orthogonal columns to the design matrix, replacing the quadratic column.

Related Experiment Videos

  • Applicable to multifactor designs, estimating effects like drift and carryover.

    • Main Results:

    • Provides a quantitative estimate of assay bias caused by nonlinearity.
    • Offers a confidence interval for the estimated deviation.
    • Demonstrates the method's utility with a manual ammonia assay dataset.

    Conclusions:

    • The presented method quantifies assay bias due to nonlinearity, offering more practical information than simple detection.
    • It integrates linearity assessment into multifactor designs, streamlining assay validation protocols.
    • This approach enhances the reliability and interpretability of assay results.