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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

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% chance...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
Random and Systematic Errors01:20

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

You might also read

Related Articles

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

Sort by
Same author

A Two-Component <i>G</i>-Prior for Variable Selection.

Bayesian analysis·2026
Same author

Post-selection inference in regression models for group testing data.

Biometrics·2024
Same author

Detecting responsible nodes in differential Bayesian networks.

Statistics in medicine·2024
Same author

Parametric modal regression with error in covariates.

Biometrical journal. Biometrische Zeitschrift·2024
Same author

Local polynomial regression for pooled response data.

Journal of nonparametric statistics·2021
Same author

Corrected score methods for estimating Bayesian networks with error-prone nodes.

Statistics in medicine·2021

Related Experiment Video

Updated: Jul 2, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Diagnosis of random-effect model misspecification in generalized linear mixed models for binary response.

Xianzheng Huang1

  • 1Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, USA. huang@stat.sc.edu

Biometrics
|September 2, 2008
PubMed
Summary

We developed a new diagnostic method to check for errors in random-effect models for clustered binary data analyzed with generalized linear mixed models (GLMMs). This ensures more reliable statistical inference in health studies.

Related Experiment Videos

Last Updated: Jul 2, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Generalized linear mixed models (GLMMs) are essential for analyzing clustered data.
  • The accuracy of GLMM inference heavily relies on the correct specification of random-effect models.
  • Misspecification of random-effect models can lead to invalid statistical conclusions.

Purpose of the Study:

  • To propose a novel diagnostic method for detecting random-effect model misspecification in GLMMs.
  • To address the critical issue of model validity in the analysis of clustered binary response data.
  • To enhance the reliability of statistical inference in complex clustered data settings.

Main Methods:

  • Development of a new diagnostic test specifically for random-effect model misspecification.
  • Theoretical justification for the proposed diagnostic method.
  • Finite sample performance evaluation through extensive simulations.
  • Application of the diagnostic method to real-world clustered binary data.

Main Results:

  • The proposed diagnostic method effectively identifies random-effect model misspecification in GLMMs.
  • Simulation studies demonstrate the method's reliable performance in finite samples.
  • The diagnostic tool provides a practical approach for assessing model validity.
  • The method was successfully applied to a longitudinal respiratory infection study dataset.

Conclusions:

  • The proposed diagnostic method is a valuable tool for ensuring the validity of GLMM analyses with clustered binary data.
  • Accurate random-effect model specification is crucial for reliable statistical inference.
  • This method enhances the trustworthiness of findings from health and epidemiological research using GLMMs.