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

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

1.6K
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
1.6K
Variability: Analysis01:11

Variability: Analysis

598
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
598
Significance Testing: Overview01:04

Significance Testing: Overview

12.9K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
12.9K
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

543
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...
543
Variation01:19

Variation

8.2K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.2K
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

653
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%...
653

You might also read

Related Articles

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

Sort by
Same author

Ultrasound-Informed State Estimation of Wrist Tremor Dynamics via Koopman Operator for Personalized Sensory Peripheral Nerve Stimulation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Borrowing information from an unidentifiable model: Guaranteed efficiency gain with a dichotomized outcome in the external data.

Biometrics·2026
Same author

Higher educational attainment in Huntington disease families: evidence from the Enroll-HD study.

Orphanet journal of rare diseases·2026
Same author

A KL-divergence-based test for elliptical distribution.

Journal of nonparametric statistics·2026
Same author

KAISER CRITERION IN FACTOR MODELS.

Acta mathematica Sinica, English series·2026
Same author

COMMUNITY EXTRACTION OF NETWORK DATA UNDER STOCHASTIC BLOCK MODELS.

Statistica Sinica·2026

Related Experiment Video

Updated: Mar 10, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K

Testing and Quantifying Site-Level Variability in Diagnostic Sensitivity of an Anchor Variable.

Seungchul Baek1, Yanyuan Ma2, Tanya P Garcia3

  • 1Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, MD, USA.

Statistics in Medicine
|March 9, 2026
PubMed
Summary
This summary is machine-generated.

Multi-site clinical studies face inconsistent disease classification due to varying diagnostic assessments. This research introduces a statistical model to test and quantify diagnostic sensitivity variability across sites, improving research consistency.

Keywords:
Huntington diseaseLaplace approximationmild cognitive impairmentmixed modelsvariance component

More Related Videos

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.8K
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

16.4K

Related Experiment Videos

Last Updated: Mar 10, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.8K
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

16.4K

Area of Science:

  • Biostatistics
  • Clinical Research Methodology
  • Epidemiology

Background:

  • Multi-site clinical research often suffers from inconsistent disease classification due to variations in diagnostic assessments across different study locations.
  • Standardized criteria and instruments do not always eliminate site-specific diagnostic variability, impacting the reliability of research findings.
  • Anchor variables, which reliably identify disease when positive but are uninformative when negative, present a unique challenge in assessing this variability.

Purpose of the Study:

  • To develop and validate a statistical framework for testing and quantifying diagnostic sensitivity variability across multiple research sites.
  • To address the challenge of inconsistent disease classification in multi-site studies, particularly when using anchor variables.
  • To provide methods for more consistent and reliable inference in collaborative clinical research settings.

Main Methods:

  • Introduction of a random effects model to estimate site-specific diagnostic sensitivity.
  • Development of likelihood-based estimation and hypothesis testing methods, incorporating validation data for parameter identifiability.
  • Application of Laplace approximation and the Expectation-Maximization (EM) algorithm to handle computational complexities in the likelihood function.
  • Construction of likelihood ratio and score tests to manage boundary constraints in hypothesis testing.

Main Results:

  • Simulation studies confirmed accurate parameter estimation, appropriate test size, and adequate statistical power in finite samples.
  • Application to a Huntington disease cohort for mild cognitive impairment diagnosis revealed significant differences in diagnostic sensitivity across sites.
  • The study provided strong statistical evidence of heterogeneity in diagnostic sensitivity among participating research sites.

Conclusions:

  • The proposed statistical framework offers a principled approach to rigorously test and quantify variability in diagnostic sensitivity across research sites.
  • This methodology enhances the consistency and reliability of disease classification in multi-site clinical research.
  • The findings support more robust and accurate inference in collaborative studies by accounting for site-level diagnostic differences.