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

Ordinal Level of Measurement00:55

Ordinal Level of Measurement

23.6K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using an ordinal scale are similar to nominal scale data, but there is one major difference. The ordinal scale data can be ordered. An example of ordinal scale data is a list of the top five national parks...
23.6K
Ranks01:02

Ranks

236
Unlike parametric methods, nonparametric statistics are ideal for nominal and ordinal data, requiring fewer assumptions about the population's nature or distribution. This makes nonparametric methods easier to apply and interpret, as they do not depend on parameters like mean or standard deviation. One common approach in nonparametric analysis is to sort data according to a specific criterion. For instance, we might arrange weather data from hottest to coldest days in a month or rank cities...
236
Nominal Level of Measurement00:56

Nominal Level of Measurement

28.4K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. Not every statistical operation can be used with every set of data. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
The data that cannot be measured but can be grouped into categories fall under the nominal level of measurement. Data that is measured using a nominal...
28.4K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

189
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...
189
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

38
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...
38
Compartment Models: Two-Compartment Model01:20

Compartment Models: Two-Compartment Model

5.5K
The two-compartment model divides the body into central and peripheral compartments to account for varying blood perfusion rates among organs and tissues, affecting drug distribution. The central compartment includes blood and highly perfused tissues with rapid drug distribution, while the peripheral compartment contains tissues with slower drug distribution. After a single IV bolus dose, the drug concentration is high in plasma and low in tissues. The drug distribution between compartments...
5.5K

You might also read

Related Articles

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

Sort by
Same author

Pathologic Response and Outcomes After Neoadjuvant Chemotherapy in Gastric Cancer: A NCDB Analysis.

Journal of surgical oncology·2026
Same author

Measurement of muscle passive stiffness in vibration-exposed groundskeepers.

International archives of occupational and environmental health·2026
Same author

BCGLMs: Bayesian modeling for disease prediction using compositional microbiome features.

Bioinformatics advances·2026
Same author

Persistent poverty, glycemic control and adverse COVID-19 outcomes: a retrospective study using real-world data.

BMC public health·2025
Same author

Handling rescue therapy in myasthenia gravis clinical trials: why it matters and why you should care.

Annals of clinical and translational neurology·2025
Same author

Altered Bacteria Abundance Is Associated With Outcomes in Head and Neck Squamous Cell Carcinoma.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2025
Same journal

Regression analysis of misclassified current status data with potentially unknown test accuracy.

Statistical methods in medical research·2026
Same journal

Bayesian multivariate linear mixed-effects models with varied association structures.

Statistical methods in medical research·2026
Same journal

Inference about the ratio of age-standardized rates between two overlapping populations.

Statistical methods in medical research·2026
Same journal

A robust neural network with random effects for subject-specific prediction of clustered count data.

Statistical methods in medical research·2026
Same journal

A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.

Statistical methods in medical research·2026
Same journal

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.

Statistical methods in medical research·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K

Bayesian compositional models for ordinal response.

Li Zhang1, Xinyan Zhang2, Justin M Leach1

  • 1Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA.

Statistical Methods in Medical Research
|April 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces Bayesian Compositional Models for Ordinal Response to analyze compositional data, outperforming existing methods in parameter estimation and prediction for microbiome and medical studies.

Keywords:
Compositional dataHamiltonian Monte CarloMCMCmicrobiomesum-to-zero restriction

More Related Videos

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.3K
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

Related Experiment Videos

Last Updated: Jun 28, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.5K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.3K
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

Area of Science:

  • Statistics
  • Bioinformatics
  • Computational Biology

Background:

  • Ordinal responses are common in medicine and biology.
  • Predictors are often compositional (fixed-sum), like microbiome relative abundance.
  • Existing models fail to account for compositional constraints and predictor correlations.

Purpose of the Study:

  • To propose a novel Bayesian method for analyzing ordinal responses with compositional predictors.
  • To address the limitations of conventional models in handling fixed-sum and correlated predictors.
  • To provide a robust framework for microbiome and other biological data analysis.

Main Methods:

  • Developed Bayesian Compositional Models for Ordinal Response (BCO).
  • Utilized a structured regularized horseshoe prior for coefficients.
  • Implemented a soft sum-to-zero restriction on coefficients via prior distribution.
  • Employed Hamiltonian Monte Carlo algorithm in R package rstan.

Main Results:

  • Proposed BCO method demonstrated superior performance over existing methods.
  • Outperformed in both parameter estimation and predictive accuracy.
  • Successfully identified microorganisms linked to inflammatory bowel disease levels in HMP2Data.

Conclusions:

  • The BCO method effectively analyzes ordinal responses with compositional predictors.
  • The approach offers improved accuracy for microbiome and similar biological data.
  • Reproducible code and data are available for the proposed method.