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

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

855
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
855
Longitudinal Studies01:26

Longitudinal Studies

300
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...
300
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

774
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...
774
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

228
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.
228
Longitudinal Research02:20

Longitudinal Research

12.8K
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...
12.8K
Prediction Intervals01:03

Prediction Intervals

2.5K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.5K

You might also read

Related Articles

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

Sort by
Same author

Trajectory Modelling of Prognostic Biomarkers Linked to Liver Cancer Risk: A Systematic Review.

Liver cancer·2026
Same author

Randomised Controlled Feasibility Trial of Face-To-Face Diabetes Self-Management Education Shows High Completion Rates Are Needed to Improve Patient-Reported Outcomes.

Diabetes, obesity & metabolism·2026
Same author

Real-world completion of diabetes self management education and its impact on patient-reported outcomes: A scoping review.

Diabetic medicine : a journal of the British Diabetic Association·2026
Same author

Learning from the outliers: A longitudinal ecological study of social and spatial inequalities in older adult influenza vaccination and hospitalisation (Cheshire and Merseyside, UK, 2018-19 to 2023-24).

Vaccine·2026
Same author

Phase 1/2 trials of donor regulatory T cells for the treatment of steroid-refractory chronic graft-versus-host disease.

Blood advances·2026
Same author

Algorithmic antibiotic decision-making in urinary tract infection using prescriber-informed prediction of treatment utility.

NPJ digital medicine·2026
Same journal

A Bayesian functional concurrent zero-inflated Dirichlet-multinomial regression model with application to infant microbiome.

Biostatistics (Oxford, England)·2026
Same journal

Towards optimal environmental policies: policy learning under arbitrary bipartite network interference.

Biostatistics (Oxford, England)·2026
Same journal

Multilevel functional quantile principal component analysis.

Biostatistics (Oxford, England)·2026
Same journal

Adaptive transfer learning for time-to-event modeling with applications in disease risk assessment.

Biostatistics (Oxford, England)·2026
Same journal

High-dimensional test for one-sided hypotheses.

Biostatistics (Oxford, England)·2026
Same journal

NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.

Biostatistics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Nov 5, 2025

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

6.5K

Fast approximate inference for multivariate longitudinal data.

David M Hughes1, Marta García-Fiñana1, Matt P Wand2

  • 1Department of Health Data Science, Waterhouse Building, Block F, University of Liverpool, 1-5 Brownlow Street, Liverpool, L69 3GL, UK.

Biostatistics (Oxford, England)
|May 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a faster computational method for analyzing multiple health outcomes over time. The new algorithm significantly reduces processing time for large datasets, making joint modeling more accessible.

Keywords:
Bayesian computingGeneralized linear mixed modelMarkov chain Monte CarloMean field variational BayesMultivariate mixed modelsRepeated measurements

More Related Videos

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

10.8K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.1K

Related Experiment Videos

Last Updated: Nov 5, 2025

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

6.5K
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

10.8K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.1K

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Computational Statistics

Background:

  • Joint modeling of multiple longitudinal outcomes is crucial in clinical research.
  • Large datasets with numerous outcomes pose computational challenges for traditional methods.
  • Existing methods like Markov Chain Monte Carlo (MCMC) can be computationally intensive.

Purpose of the Study:

  • To develop an efficient algorithm for jointly modeling multiple longitudinal outcomes.
  • To address the computational burden associated with large-scale longitudinal data analysis.
  • To provide an accurate and computationally feasible alternative to MCMC for multivariate generalized linear mixed models.

Main Methods:

  • Development of a mean field variational Bayes algorithm.
  • Application to multivariate generalized linear mixed models.
  • Joint modeling of Gaussian, Poisson, and binary longitudinal markers.

Main Results:

  • The proposed algorithm offers substantial computational savings compared to standard MCMC.
  • The method maintains good accuracy in estimating model parameters.
  • Demonstrated effectiveness in simulation studies and clinical applications.

Conclusions:

  • Mean field variational Bayes provides an efficient approach for joint longitudinal data analysis.
  • This method enhances the feasibility of modeling multiple outcomes in large datasets.
  • The algorithm is applicable to various data types including Gaussian, Poisson, and binary outcomes.