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

Multiple Regression01:25

Multiple Regression

3.3K
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...
3.3K
Regression Analysis01:11

Regression Analysis

6.6K
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:
6.6K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

450
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
450
Goodness-of-Fit Test01:16

Goodness-of-Fit Test

5.9K
The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...
5.9K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

355
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...
355
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

337
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...
337

You might also read

Related Articles

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

Sort by
Same author

Perinatal prediction of mortality for neonates born at 22-25 weeks gestation.

Journal of perinatology : official journal of the California Perinatal Association·2026
Same author

Prospective economic evaluation ancillary to a trial of higher versus lower hemoglobin transfusion thresholds for preterm infants.

Neonatology·2026
Same author

Correlation of Oxygen Saturation Index with Oxygenation Index in Congenital Diaphragmatic Hernia: in A Secondary Analysis of a Randomized Clinical Trial.

The Journal of pediatrics·2026
Same author

Budesonide and Surfactant Therapy Versus Surfactant Alone on Incidence of Lung Disease in Preterm Infants (BEST Lung): Study Protocol for a Systematic Review and Individual Participant Data Meta-Analysis With Nested Prospective Meta-Analysis.

Acta paediatrica (Oslo, Norway : 1992)·2026
Same author

Parent-reported quality of life at school age among children born extremely preterm is associated with non-medical determinants of health and developmental outcomes.

Early human development·2026
Same author

Symptom-Based Dosing for Neonatal Opioid Withdrawal: The OPTimize NOW Randomized Clinical Trial.

JAMA·2026

Related Experiment Video

Updated: Oct 25, 2025

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

3.5K

Comments on "Intermediate and advanced topics in multilevel logistic regression analysis".

Lei Li1, Matthew A Rysavy2, Abhik Das3

  • 1Biostatistics and Epidemiology Division, RTI International, Research Triangle Park, NC, USA.

Statistics in Medicine
|August 5, 2021
PubMed
Summary

Multilevel random-effects models are useful for analyzing clustered data by separating individual and group effects. This study clarifies key concepts in these hierarchical models for better understanding and application.

More Related Videos

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

9.4K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K

Related Experiment Videos

Last Updated: Oct 25, 2025

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

3.5K
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

9.4K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Multilevel random-effects models are increasingly used for clustered data analysis.
  • These models allow quantification of within- and between-cluster variation.
  • They also separate individual-level and cluster-level covariate effects.

Purpose of the Study:

  • To clarify important concepts in multilevel random-effects models.
  • To provide a clearer understanding of tools and approaches for analyzing hierarchical data.
  • To address potential ambiguities in existing reviews.

Main Methods:

  • The study focuses on conceptual clarification rather than new data analysis.
  • It involves a critical review and explanation of established statistical principles.
  • The discussion centers on the interpretation of multilevel model outputs.

Main Results:

  • Key ideas regarding the interpretation of variance components need further explanation.
  • The distinction between fixed and random effects in hierarchical structures requires careful delineation.
  • Understanding the implications of covariate placement (individual vs. cluster level) is crucial.

Conclusions:

  • Clarification of multilevel model concepts enhances their appropriate application.
  • Accurate interpretation of model components is vital for valid conclusions in clustered data research.
  • Further discussion can improve the utility of these powerful statistical tools.