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

Regression Analysis01:11

Regression Analysis

9.0K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
9.0K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

719
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
719
Correlation and Regression00:53

Correlation and Regression

4.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
4.2K
Multiple Regression01:25

Multiple Regression

4.4K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.4K
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
Contingency Table01:29

Contingency Table

5.0K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
5.0K

You might also read

Related Articles

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

Sort by
Same author

Dynamic case-control sampling for rapid estimation of vaccine effectiveness against an emerging infectious disease variant.

Biostatistics (Oxford, England)·2026
Same author

Mapping social determinants of health data in sub-Saharan Africa: a scoping review protocol.

BMJ open·2026
Same author

Statistics in Medicine - What's in an Estimand?

The New England journal of medicine·2025
Same author

HIV drug resistance, early treatment outcomes and impact of guidelines compliance after protease inhibitor-based second-line failure in a dedicated resistance clinic in western Kenya: a retrospective cohort study.

Journal of the International AIDS Society·2025
Same author

Challenges Faced by Perinatally-Infected Kenyan Adolescents and Youth Living with HIV During the COVID-19 Pandemic.

AIDS and behavior·2025
Same author

"Target Trial Emulation" for Observational Studies - Potential and Pitfalls.

The New England journal of medicine·2024

Related Experiment Video

Updated: Apr 12, 2026

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

8.1K

Regression Analysis for Differentially Misclassified Correlated Binary Outcomes.

Li Tang1, Robert H Lyles2, Caroline C King3

  • 1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, U.S.A.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|May 26, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method to correct for misclassified correlated binary variables in epidemiological studies. The approach uses internal validation sampling to improve the accuracy of generalized linear mixed models (GLMMs).

Keywords:
BiasDifferential misclassificationNonlinear mixed modelValidation

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.8K

Related Experiment Videos

Last Updated: Apr 12, 2026

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

8.1K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.8K

Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Misclassification of variables in epidemiological studies can lead to invalid analytic results.
  • Correlated binary response variables are particularly susceptible to misclassification.
  • Existing methods may not fully address complex differential misclassification over time.

Purpose of the Study:

  • To develop and validate a statistical approach to adjust for differential misclassification in correlated binary response variables.
  • To estimate parameters in a generalized linear mixed model (GLMM) accounting for time-dependent covariates.
  • To model the misclassification process using sensitivity and specificity parameters.

Main Methods:

  • Utilized internal validation sampling at multiple time points.
  • Employed a primary generalized linear mixed model (GLMM) for primary analysis.
  • Developed a secondary generalized linear model to capture time-varying sensitivity and specificity.
  • Incorporated subject-specific covariates in the misclassification model.

Main Results:

  • Simulation studies demonstrated the precision and validity of the proposed adjustment method.
  • The method effectively accounts for complex differential misclassification in correlated binary data.
  • Accurate estimation of model parameters was achieved even with misclassified data.

Conclusions:

  • The proposed method provides a robust approach for correcting misclassification in longitudinal epidemiological studies.
  • Accurate analysis of correlated binary outcomes is achievable with this technique.
  • The method is applicable to real-world studies, as shown by its application to bacterial vaginosis data.