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

687
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...
687
Multiple Comparison Tests01:13

Multiple Comparison Tests

4.5K
Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...
4.5K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

316
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...
316
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.3K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.3K
Two-Way ANOVA01:17

Two-Way ANOVA

3.5K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
3.5K
Longitudinal Research02:20

Longitudinal Research

13.6K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.6K

You might also read

Related Articles

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

Sort by
Same author

Correction: Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya.

PloS one·2026
Same author

Spatial disparities and associated factors of composite index of anthropometric failure for under-five children across three African countries.

Global epidemiology·2026
Same author

Supervised machine learning algorithms for classifications of gender-based violence in Somalia: a comparison of oversampling techniques.

Scientific reports·2026
Same author

Hyperbaric Oxygen Therapy Versus Intravenous Thrombolysis in the Treatment of Central Retinal Artery Occlusion: A Systematic Review and Meta-Analysis.

Journal of clinical medicine·2026
Same author

Prevalence, associated factors and geospatial patterns of HIV drug resistance in South Africa among the virally unsuppressed adults: updated results from the 2017 national HIV household survey.

BMC public health·2026
Same author

Confidence Intervals for Comparing Two Independent Folded Normals: A Case Study in Bunion Surgery.

Statistics in medicine·2026

Related Experiment Video

Updated: Mar 12, 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

3.8K

Meta-Analysis of Effect Sizes Reported at Multiple Time Points Using General Linear Mixed Model.

Alfred Musekiwa1, Samuel O M Manda1,2, Henry G Mwambi1

  • 1School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, South Africa.

Plos One
|November 1, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces advanced meta-analysis methods for longitudinal data, improving parameter estimates by accounting for the dependence between effect sizes. Accounting for within-study correlation enhances precision in meta-analysis of longitudinal studies.

More Related Videos

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Related Experiment Videos

Last Updated: Mar 12, 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

3.8K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

1.3K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Longitudinal Data Analysis

Background:

  • Traditional meta-analysis of longitudinal studies often uses separate univariate analyses for each time point.
  • This approach overlooks the inherent dependence between effect sizes measured over time within studies.
  • Ignoring this correlation can lead to less precise parameter estimates in meta-analysis.

Purpose of the Study:

  • To develop and demonstrate a meta-analysis method that accounts for the dependence between longitudinal effect sizes.
  • To contrast various covariance structures for modeling within-study and between-study dependence.
  • To apply the proposed methods to a real-world meta-analysis of cancer treatment trials.

Main Methods:

  • Conducting meta-analysis of longitudinal effect sizes by modeling dependence structures.
  • Comparing different within-study and between-study covariance structures.
  • Utilizing a practical example from 17 cancer treatment trials with survival data at multiple time points (6, 12, 18, 24 months).

Main Results:

  • The analysis of the cancer treatment trials indicated a benefit from accounting for within-study serial correlation.
  • This suggests that incorporating dependence structures improves the precision of meta-analysis estimates.
  • Further simulations are recommended to validate these findings across different scenarios.

Conclusions:

  • Advanced meta-analysis techniques that model dependence improve the accuracy of parameter estimates in longitudinal studies.
  • Accounting for within-study serial correlation is beneficial for meta-analysis of time-dependent outcomes.
  • The proposed methods offer a more robust approach for synthesizing evidence from longitudinal research.