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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

175
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...
175
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

168
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...
168
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

35
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...
35
Two-Way ANOVA01:17

Two-Way ANOVA

2.6K
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...
2.6K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

117
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.
117
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.7K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.7K

You might also read

Related Articles

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

Sort by
Same author

Venetoclax combined with intensive chemotherapy as induction chemotherapy in newly diagnosed AML with FLT3-ITD-mutation.

Blood cancer journal·2026
Same author

Corrigendum to "Fish Meteorin-like factor promotes migration, proliferation and phagocytic activity of monocytes/macrophages" [Fish Shellfish Immunol.171 (2026) 111193].

Fish & shellfish immunology·2026
Same author

A Mechanism-Guided Strategy by Weighting Key Reaction States for Rational Engineering of ω-Transaminase toward Bulky Chiral Amines.

Organic letters·2026
Same author

Performance of Nitrogen Removal and Biofilm-Associated Microbial Community in a Compact Marine Shrimp Recirculating Aquaculture System with MBBR.

Microorganisms·2026
Same author

Advantages of combining multiple eye-tracking paradigms for distinguishing young autistic from non-autistic children.

Molecular autism·2026
Same author

Seven-day Venetoclax Combined With Dose-adjusted Intensive Chemotherapy as Induction Treatment in Newly Diagnosed Acute Myeloid Leukemia.

Clinical lymphoma, myeloma & leukemia·2026
Same journal

Bayesian Variable Shrinkage and Selection in Compositional Data Regression: Application to Oral Microbiome.

Journal of the Indian Society for Probability and Statistics·2024
See all related articles

Related Experiment Video

Updated: Jun 18, 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

3.3K

Single-Index Mixed-Effects Model for Asymmetric Bivariate Clustered Data.

Weihua Zhao1, Dipankar Bandyopadhyay2, Heng Lian3

  • 1School of Sciences, Nantong University, Nantong, China.

Journal of the Indian Society for Probability and Statistics
|July 29, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel non-linear mixed model to analyze periodontal disease (PD) progression, offering a more accurate risk assessment for Type-2 diabetics. The model effectively handles complex data, improving inference for periodontal health outcomes.

Keywords:
Asymmetric Laplace distributionClustered dataEM algorithmRandom-effectsSingle-index model

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
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

5.8K

Related Experiment Videos

Last Updated: Jun 18, 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

3.3K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
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

5.8K

Area of Science:

  • Biostatistics
  • Dental Research
  • Epidemiology

Background:

  • Periodontal disease (PD) progression studies often use linear mixed models (LMMs) with bivariate endpoints like probed pocket depth (PPD) and clinical attachment level (CAL).
  • Violations of normality assumptions in LMMs can lead to imprecise inferences, and the linear assumption may not capture the true response-covariate relationship.
  • Existing methods may not provide a comprehensive summary of PD risk from covariates.

Purpose of the Study:

  • To develop a non-linear mixed model framework for analyzing asymmetric, clustered bivariate responses (PPD and CAL) in periodontal disease.
  • To provide a one-number summary of PD risk by modeling non-linear covariate relationships.
  • To address limitations of traditional LMMs in handling non-normal and non-linear data in PD research.

Main Methods:

  • Utilized a non-linear mixed model with multivariate asymmetric Laplace distribution (ALD) for random terms.
  • Employed a single-index model with polynomial spline approximations to capture non-linear relationships.
  • Developed an EM-type algorithm for maximum-likelihood estimation and established large sample theoretical properties.
  • Validated the approach through simulation studies and application to a PD study in Type-2 diabetic African-Americans.

Main Results:

  • The proposed model and estimation algorithm effectively handle asymmetric, heavy-tailed data, including outliers.
  • Simulation studies demonstrated the efficiency of the estimators in finite-sample scenarios.
  • The methodology provides a more accurate assessment of periodontal disease progression and risk.

Conclusions:

  • The novel non-linear mixed model offers a robust framework for analyzing complex periodontal disease data.
  • This approach improves risk assessment and provides a more nuanced understanding of PD progression, particularly in diabetic populations.
  • The developed EM-type algorithm ensures efficient and reliable statistical inference.