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

Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.7K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.7K
Longitudinal Studies01:26

Longitudinal Studies

578
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...
578
Prediction Intervals01:03

Prediction Intervals

3.5K
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. 
3.5K
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

46.4K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
46.4K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

522
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...
522
Classification of Systems-II01:31

Classification of Systems-II

537
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
537

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

Source apportionment of reactive nitrogen in fog vs. rain using isotope ratios and Bayesian modeling (Ore and Eagle Mts., Central Europe).

Journal of hazardous materials·2026
Same author

Efficacy and Safety of DOACs in Patients with Atrial Fibrillation and History of Falls or Risk of Falls: The Liverpool AF-Falls Project. A Systematic Review and Bayesian Network Meta-analysis.

Drugs & aging·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

Does white matter structure relate to hemispheric language lateralization? A systematic review.

Brain communications·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

Related Experiment Video

Updated: Feb 25, 2026

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K

Dynamic classification using credible intervals in longitudinal discriminant analysis.

David M Hughes1, Arnošt Komárek2, Laura J Bonnett1

  • 1Department of Biostatistics, University of Liverpool, Liverpool, U.K.

Statistics in Medicine
|August 2, 2017
PubMed
Summary

This study introduces a new method for prognostic classification using longitudinal biomarker data. Incorporating credible intervals improves accuracy and reduces false positives in identifying patient groups, enhancing clinical decision-making.

Keywords:
allocation schemecredible intervalslongitudinal discriminant analysis

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

6.8K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

573

Related Experiment Videos

Last Updated: Feb 25, 2026

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.3K
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

6.8K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

573

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Medical Prognostics

Background:

  • Longitudinal discriminant analysis classifies subjects into prognostic groups using biomarker data.
  • Current methods use Bayesian estimates from generalized linear mixed models for dynamic patient allocation.
  • Estimated group probabilities vary in precision over time and between patients.

Purpose of the Study:

  • To propose a novel allocation rule for dynamic longitudinal discriminant analysis incorporating credible intervals.
  • To enhance the positive predictive value of prognostic tests by reducing false positives.
  • To improve classification accuracy and clinician confidence through dynamic stopping rules.

Main Methods:

  • Development of a new allocation rule based on credible intervals of group membership probabilities.
  • Utilizing multivariate generalized linear mixed models for biomarker longitudinal evolution.
  • Implementing dynamic stopping rules for patient classification instead of fixed time points.

Main Results:

  • The proposed rule decreases false positives, thereby improving the positive predictive value of prognostic tests.
  • Delaying classification for some patients improves the accuracy for those who are classified.
  • Dynamic stopping rules demonstrate higher accuracy compared to fixed time-point decisions.
  • Epilepsy patient data showed more accurate identification of uncontrolled seizure cases using credible intervals.

Conclusions:

  • Incorporating credible intervals into dynamic longitudinal discriminant analysis enhances prognostic accuracy.
  • Dynamic allocation and stopping rules offer improved precision and reliability in patient classification.
  • The methodology shows promise for clinical applications, such as identifying epilepsy patients needing intervention.