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

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

1.3K
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
1.3K
Confirmation Biases01:31

Confirmation Biases

8.1K
The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
8.1K
Hindsight Biases01:12

Hindsight Biases

4.2K
Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
4.2K
Bias01:22

Bias

7.3K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
7.3K
The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

55.6K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
55.6K
Renal Drug Clearance: Comparison Between Renal Excretion Methods01:08

Renal Drug Clearance: Comparison Between Renal Excretion Methods

587
Renal clearance is a critical parameter encompassing kidney filtration, secretion, and reabsorption processes. It is calculated using a specific equation to determine the rate at which the kidneys clear a drug.
Renal clearance is often associated with the renal glomerular filtration rate (GFR), which represents the rate at which plasma is filtered through the glomeruli in the kidney. When drug reabsorption is minimal and there is no active secretion, renal clearance is closely related to the...
587

You might also read

Related Articles

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

Sort by
Same author

Impact of Disease Recurrence and Outcomes following Kidney Transplantation for Glomerulonephritis: A Prospective Nationwide Cohort Study.

Kidney360·2026
Same author

Agreement Between Two Quantitative Measurement Methods When the Underlying Latent Trait Is Not Constant.

Statistics in medicine·2025
Same author

Diagnostic accuracy of carotid plaque instability by noninvasive imaging: a systematic review and meta-analysis.

European heart journal. Cardiovascular Imaging·2024
Same author

Probability of sporadic lymphangioleiomyomatosis in women presenting with spontaneous pneumothorax.

Orphanet journal of rare diseases·2023
Same author

The Bland-Altman method should not be used when one of the two measurement methods has negligible measurement errors.

PloS one·2022
Same author

Use of clinical tolerance limits for assessing agreement.

Statistical methods in medical research·2022

Related Experiment Video

Updated: Jan 25, 2026

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
05:14

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

590

Assessing bias, precision, and agreement in method comparison studies.

Patrick Taffé1

  • 1Institute of Social and Preventive Medicine, University of Lausanne, Lausanne, Switzerland.

Statistical Methods in Medical Research
|April 26, 2019
PubMed
Summary

This study enhances bias and precision assessment for measurement methods by introducing confidence bands and a new agreement index. The improved methodology facilitates formal comparisons and detailed graphical analysis of measurement agreement.

Keywords:
Agreementdifferential biaslimits of agreementmethod comparisonprecisionproportional bias

More Related Videos

Assessment of Mouse Judgment Bias through an Olfactory Digging Task
12:10

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Published on: March 4, 2022

3.1K
Author Spotlight: A Precise and Quantifiable Method for Collecting Hemolymph from Small Arthropods
03:39

Author Spotlight: A Precise and Quantifiable Method for Collecting Hemolymph from Small Arthropods

Published on: April 28, 2023

2.6K

Related Experiment Videos

Last Updated: Jan 25, 2026

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter
05:14

Comparison of Agreement and Accuracy using Binocular Wavefront Optometer with Autorefractor and Phoropter

Published on: September 16, 2025

590
Assessment of Mouse Judgment Bias through an Olfactory Digging Task
12:10

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Published on: March 4, 2022

3.1K
Author Spotlight: A Precise and Quantifiable Method for Collecting Hemolymph from Small Arthropods
03:39

Author Spotlight: A Precise and Quantifiable Method for Collecting Hemolymph from Small Arthropods

Published on: April 28, 2023

2.6K

Area of Science:

  • Biostatistics
  • Measurement Science
  • Medical Device Evaluation

Background:

  • A recent estimation procedure assesses measurement method bias and precision against a reference standard.
  • This procedure lacks confidence bands for bias and standard deviation curves, limiting formal comparisons.

Purpose of the Study:

  • To extend the existing methodology by developing simultaneous confidence bands for parameter estimation.
  • To introduce a novel index for assessing agreement between measurement methods.
  • To provide enhanced graphical tools for evaluating bias, precision, and agreement.

Main Methods:

  • The methodology involves repeated measurements on individuals for at least one measurement method.
  • It estimates differential and proportional biases effectively, even with minimal repeated measurements.
  • Repeated measurements can originate from either the new method or the reference standard.

Main Results:

  • Simultaneous confidence bands enable formal comparisons between different measurement methods.
  • A new index of agreement is proposed for more robust assessment.
  • New graphical representations aid investigators in evaluating bias, precision, and agreement.

Conclusions:

  • The extended methodology provides a more comprehensive framework for evaluating measurement methods.
  • It offers improved tools for assessing bias, precision, and agreement, enhancing data interpretation.
  • The approach is flexible regarding the source of repeated measurements, increasing its practical applicability.