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

Survival Tree01:19

Survival Tree

466
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
466
Longitudinal Studies01:26

Longitudinal Studies

650
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...
650
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

701
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...
701
Longitudinal Research02:20

Longitudinal Research

13.6K
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...
13.6K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

520
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
520
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.2K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.2K

You might also read

Related Articles

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

Sort by
Same author

An innovative 2D optical navigation workflow for percutaneous pedicle screw fixation in thoracolumbar fractures: comparison with O-arm 3D navigation.

Frontiers in surgery·2026
Same author

Decoding cancer circulating transcriptomic signatures with language models.

Nature communications·2026
Same author

Site-specific neoepitope induction by RNA editing reprograms tumor immunogenicity.

Frontiers in immunology·2026
Same author

Features predicting data exclusion in imaging studies of Alzheimer's disease.

Alzheimer's & dementia (Amsterdam, Netherlands)·2026
Same author

No cognitive or psychological impact from returning research Alzheimer disease biomarkers: A delayed-start, noninferiority, randomized clinical trial.

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

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026

Related Experiment Video

Updated: Mar 25, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

Predicting Clinical Binary Outcome Using Multivariate Longitudinal Data: Application to Patients with Newly Diagnosed

Feng Gao1, J Philip Miller2, Julia A Beiser3

  • 1Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St. Louis, MO, USA; Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.

Journal of Biometrics & Biostatistics
|February 24, 2016
PubMed
Summary

Identifying rapid vision loss in primary open angle glaucoma (POAG) is key. A new model accurately predicts visual field progression by analyzing patient data over time, aiding early intervention for POAG patients.

Keywords:
Functional principal component analysisLatent class growth modelMultivariate longitudinal dataPrimary open angle glaucoma

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.0K

Related Experiment Videos

Last Updated: Mar 25, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.0K

Area of Science:

  • Ophthalmology
  • Biostatistics
  • Medical Data Science

Background:

  • Primary open angle glaucoma (POAG) is a leading cause of irreversible blindness.
  • Predicting the rate of visual field (VF) loss in POAG patients is critical for timely intervention.
  • Current methods for predicting VF progression have limitations.

Purpose of the Study:

  • To develop and validate a novel statistical model for predicting binary visual field progression in newly diagnosed POAG patients.
  • To identify distinct subgroups of POAG patients based on their longitudinal visual field changes.
  • To improve the accuracy of identifying patients at high risk for rapid vision loss.

Main Methods:

  • Latent Class Growth Modeling (LCGM) was employed to analyze longitudinal data.
  • Functional Principal Component (FPC) analysis summarized trajectories of Mean Deviation (MD) and Pattern Standard Deviation (PSD).
  • Estimated FPC scores identified latent classes representing distinct MD and PSD progression patterns.

Main Results:

  • The LCGM, applied to Ocular Hypertension Treatment Study (OHTS) data, identified 4 distinct latent classes.
  • The model accurately differentiated between POAG participants who did and did not experience VF progression.
  • Incorporating longitudinal MD and PSD data significantly improved prediction accuracy.

Conclusions:

  • The developed LCGM provides an effective tool for predicting visual field progression in POAG.
  • This approach allows for more precise identification of high-risk individuals, enabling personalized treatment strategies.
  • Early identification of progressive visual field loss is crucial for preventing blindness in POAG.