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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

413
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
413
Longitudinal Studies01:26

Longitudinal Studies

156
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
156
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

122
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.
122
Longitudinal Research02:20

Longitudinal Research

11.9K
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...
11.9K
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
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

37
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...
37

You might also read

Related Articles

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

Sort by
Same author

Mean Arterial Pressure During the First 24 Hours After Cardiac Surgery and Acute Kidney Injury: An Observational Cohort Study.

Journal of cardiothoracic and vascular anesthesia·2026
Same author

Swab Testing to Optimize Pneumonia Treatment With Empiric Vancomycin: A Randomized Controlled Trial.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Afternoon panel discussion).

Clinical trials (London, England)·2026
Same author

Comparison of machine learning methods for prediction of venous thromboembolism among hospitalized adults.

Journal of hospital medicine·2026
Same author

Determining the Physiological Threshold for Angina (ORBITA-FIRE): A Double-Blind, Randomized, Placebo-Controlled Study.

Circulation·2026
Same author

Development and external validation of the NEO-READY model to predict date of discharge among premature neonatal intensive care patients.

medRxiv : the preprint server for health sciences·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
Same journal

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same journal

Rejoinder to Commentaries on "A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events".

Statistics in medicine·2026
Same journal

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Statistics in medicine·2026
Same journal

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 24, 2025

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

10.7K

Bayesian transition models for ordinal longitudinal outcomes.

Maximilian D Rohde1, Benjamin French1, Thomas G Stewart2

  • 1Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.

Statistics in Medicine
|June 10, 2024
PubMed
Summary
This summary is machine-generated.

Bayesian ordinal transition models offer a flexible approach for analyzing longitudinal data in clinical trials, especially for COVID-19 research. These methods enhance statistical efficiency for ordinal outcomes.

Keywords:
Bayesian modelingclinical trialsordinal longitudinal outcomestransition models

More Related Videos

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

Related Experiment Videos

Last Updated: Jun 24, 2025

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

10.7K
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

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Epidemiology

Background:

  • Ordinal longitudinal outcomes are increasingly prevalent in clinical research.
  • These data types are information-rich and can improve study efficiency if analyzed appropriately.
  • The COVID-19 pandemic has highlighted the need for robust methods to analyze such data.

Purpose of the Study:

  • To introduce Bayesian ordinal transition models as a flexible framework for analyzing ordinal longitudinal outcomes.
  • To provide theoretical underpinnings and practical R code examples for implementing these models.
  • To demonstrate the application of these models using data from the Adaptive COVID-19 Treatment Trial (ACTT-1).

Main Methods:

  • Development of Bayesian ordinal transition models from first principles.
  • Application of the models to longitudinal data with ordered categories.
  • Utilizing R for statistical analysis and code examples.

Main Results:

  • The proposed models offer a principled and flexible approach to analyzing ordinal longitudinal data.
  • Demonstrated increased statistical efficiency compared to standard methods.
  • Successful application to real-world COVID-19 clinical trial data (ACTT-1).

Conclusions:

  • Bayesian ordinal transition models are recommended for analyzing ordinal longitudinal outcomes in clinical research.
  • These models provide a valuable alternative or complement to traditional time-to-event analyses.
  • Researchers are encouraged to adopt these methods for enhanced statistical power and richer data interpretation.