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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

200
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...
200
Randomized Experiments01:13

Randomized Experiments

7.0K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.0K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

207
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
207
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

2.5K
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).
2.5K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

136
Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
136
Study Design in Statistics01:15

Study Design in Statistics

8.2K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
8.2K

You might also read

Related Articles

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

Sort by
Same author

Cost-Effectiveness of Remote Cognitive Behavioral Based Therapy for Chronic Pain among People with High-Impact Chronic Pain.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research·2026
Same author

Substitution Patterns After Discontinuation of CNS-Active Medications in Older Adults in Primary Care.

Journal of the American Geriatrics Society·2026
Same author

Effect of self-management interventions on high impact chronic pain prevalence.

The journal of pain·2026
Same author

Using Multistate Models and Qualitative Interviews to Comprehensively Characterize Substance Use Disorder Care Transitions in a US Health Care System: Protocol for a Mixed-Methods Study.

JMIR research protocols·2026
Same author

EFFICIENT AND MULTIPLY ROBUST RISK ESTIMATION UNDER GENERAL FORMS OF DATASET SHIFT.

Annals of statistics·2026
Same author

Predictors of patient treatment adherence and moderators of response for a randomized clinical trial comparing remote cognitive behavioral therapy approaches for high-impact chronic musculoskeletal pain.

Pain·2026

Related Experiment Video

Updated: Jul 11, 2025

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.2K

Evaluating tests for cluster-randomized trials with few clusters under generalized linear mixed models with covariate

Hongxiang Qiu1, Andrea J Cook2,3, Jennifer F Bobb2,3

  • 1Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan, USA.

Statistics in Medicine
|November 7, 2023
PubMed
Summary

For small or moderate cluster numbers in generalized linear mixed models (GLMM), likelihood ratio tests with between-within degrees of freedom maintain accurate type I error rates when adjusting for few covariates in cluster-randomized trials (CRTs).

Keywords:
GLMMcluster-randomized trialcovariate adjustmentsmall number of clusters

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.9K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Related Experiment Videos

Last Updated: Jul 11, 2025

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.2K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

5.9K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.0K

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Modeling

Background:

  • Generalized linear mixed models (GLMM) are standard for clustered data.
  • Small to moderate cluster numbers can lead to inflated Type I error rates with standard tests.
  • The performance of GLMM tests for count outcomes or with covariate adjustment in small samples is unclear.

Purpose of the Study:

  • To evaluate GLMM-based tests for treatment effects in cluster-randomized trials (CRTs) with small (10) or moderate (20) clusters.
  • To assess test performance under various covariate adjustment scenarios for binary and count outcomes.
  • To identify reliable statistical tests for CRTs with limited cluster numbers.

Main Methods:

  • Conducted simulations of parallel-group CRTs with 10 or 20 clusters.
  • Evaluated GLMM-based tests for binary and count outcomes.
  • Included scenarios with person-level and cluster-level covariate adjustments.
  • Assessed Type I error rates under different numbers of covariates.

Main Results:

  • Likelihood ratio tests with between-within denominator degrees of freedom demonstrated Type I error rates near the nominal level when the intraclass correlation was non-negligible and covariate numbers were small (≤2).
  • Test performance varied considerably with a moderate number of covariates (around 5), with no single method performing optimally across all scenarios.
  • Elevated Type I error rates were observed with standard tests when cluster numbers were small to moderate.

Conclusions:

  • For small to moderate cluster numbers in CRTs, likelihood ratio tests using between-within denominator degrees of freedom are recommended when adjusting for a few covariates.
  • Limiting covariate adjustment to no more than a few is advised to maintain reliable statistical inference.
  • Further research may be needed to identify robust methods for complex covariate adjustment scenarios in CRTs.