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

Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.6K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
1.6K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

233
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
233
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

285
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
285
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.7K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.7K
Data Validation01:15

Data Validation

194
Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
Key parameters for method validation include:
194
Contaminants and Errors01:16

Contaminants and Errors

123
Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...
123

You might also read

Related Articles

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

Sort by
Same author

Longitudinal assessment of clinical risk scores for HCC in patients with cirrhosis.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2026
Same author

Towards early detection of pancreatic cancer: The current status of cohort studies by the Diabetes-Pancreatic Ductal AdenoCarcinoma Working Group.

Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.]·2026
Same author

Development and validation of risk stratification models for hepatocellular cancer: A framework from the translational liver cancer consortium.

Hepatology (Baltimore, Md.)·2026
Same author

Validation of longitudinal biomarker screening algorithms for HCC detection in patients with cirrhosis.

Hepatology communications·2026
Same author

Reply.

Gastroenterology·2026
Same author

Validation of Texas Hepatocellular Carcinoma Consortium Risk Index in the Hepatocellular Carcinoma Early Detection Strategy Study.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association·2026

Related Experiment Video

Updated: Jul 28, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K

A Flexible Method for Diagnostic Accuracy with Biomarker Measurement Error.

Ching-Yun Wang1, Ziding Feng1

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, P.O. Box 19024, Seattle, WA 98109-1024, USA.

Mathematics (Basel, Switzerland)
|May 30, 2023
PubMed
Summary

Measurement errors in diagnostic biomarkers can bias accuracy estimates. This study introduces a flexible skew-normal distribution method to correct for these biases, improving diagnostic performance evaluation for biomarkers like those in pancreatic cancer studies.

Keywords:
biomarkerscorrection for attenuationmeasurement error

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

36.6K

Related Experiment Videos

Last Updated: Jul 28, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

36.6K

Area of Science:

  • Biostatistics
  • Biomarker Discovery
  • Diagnostic Accuracy

Background:

  • Diagnostic biomarkers are crucial for disease detection but are susceptible to measurement errors from assay variability.
  • Ignoring these errors can lead to biased diagnostic accuracy measures (e.g., area under the ROC curve, sensitivity, specificity), misrepresenting biomarker efficacy.
  • Current correction methods may fail with non-normally distributed biomarker data.

Purpose of the Study:

  • To develop a flexible statistical method to correct for bias in diagnostic accuracy estimation caused by biomarker measurement error.
  • To account for skewed biomarker distributions, which are common in real-world applications.
  • To provide a more reliable approach for evaluating diagnostic biomarker performance.

Main Methods:

  • Development of a novel bias correction method utilizing skew-normal biomarker distributions.
  • Extensive simulation studies to evaluate the finite sample performance of the proposed method.
  • Application of the developed method to a real-world pancreatic cancer biomarker dataset.

Main Results:

  • The proposed skew-normal based method effectively corrects for bias in estimating diagnostic accuracy measures.
  • Simulation results demonstrate the robustness and improved performance of the new method compared to existing approaches, especially with skewed data.
  • The method provides more accurate estimates of area under the ROC curve, sensitivity, and specificity.

Conclusions:

  • The developed method offers a flexible and accurate approach to address measurement error in diagnostic biomarkers, particularly when data are not normally distributed.
  • This enhances the reliable assessment of diagnostic biomarker performance, crucial for clinical decision-making.
  • The approach is valuable for studies involving complex biomarker data, such as in oncology research.