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

Longitudinal Studies01:26

Longitudinal Studies

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...
Longitudinal Research02:20

Longitudinal Research

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

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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 from...
Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares the...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

You might also read

Related Articles

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

Sort by
Same author

Hydrogen-bond mediated transitional adlayer of glycine on Si(111)7 x 7 at room temperature.

The Journal of chemical physics·2009
Same author

Nematicidal principles from two species of lamiaceae.

Journal of nematology·2009
Same author

Use of sequence motifs as barcodes and secondary structures of internal transcribed spacer 2 (ITS2, rDNA) for identification of the Indian liver fluke, Fasciola (Trematoda: Fasciolidae).

Bioinformation·2009
Same author

Molecular characterization of Gastrodiscoides hominis (Platyhelminthes: Trematoda: Digenea) inferred from ITS rDNA sequence analysis.

Parasitology research·2009
Same author

Determination of total cationic and total anionic arsenic species in oyster tissue using microwave-assisted extraction followed by HPLC-ICP-MS.

Talanta·2008
Same author

Generic tacrolimus (Pan Graf) in renal transplantation: an experience of 155 recipients in India.

Transplantation proceedings·2008
Same journal

A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme.

Statistical methods in medical research·2026
Same journal

Efficient semi-supervised estimation of optimal individualized treatment regimes with survival outcome.

Statistical methods in medical research·2026
Same journal

Asymptotic online FWER control for dependent test statistics.

Statistical methods in medical research·2026
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
See all related articles

Related Experiment Video

Updated: Jun 15, 2026

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

A review of multivariate longitudinal data analysis.

S Bandyopadhyay1, B Ganguli, A Chatterjee

  • 1Indian Institute of Public Health, C/O Indian Institute of Health and Family Welfare, Vengal Rao Nagar, Hyderabad, India. bansouvik@gmail.com

Statistical Methods in Medical Research
|March 10, 2010
PubMed
Summary
This summary is machine-generated.

Analyzing multivariate longitudinal data requires specialized methods due to correlated outcomes over time. This review covers three approaches for handling complex biomedical and public health research data.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: Jun 15, 2026

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

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Biostatistics
  • Epidemiology
  • Public Health Research

Background:

  • Biomedical and public health studies frequently involve observing multiple outcomes repeatedly over time.
  • This generates multivariate longitudinal data, necessitating specialized analytical techniques due to inherent correlations within and between responses.

Purpose of the Study:

  • To review and compare three distinct methodologies for analyzing multivariate longitudinal data.
  • To provide insights into the theory, practical applications, and available software for each approach.
  • To illustrate these methods using a novel multivariate longitudinal dataset.

Main Methods:

  • Method 1: Applying univariate longitudinal analysis to a single summarized outcome.
  • Method 2: Estimating regression coefficients without explicit modeling of the data's covariance structure.
  • Method 3: Employing a joint multivariate model to integrate all outcomes.

Main Results:

  • The study reviews three analytical approaches for multivariate longitudinal data.
  • Each method is discussed concerning its theoretical underpinnings, application scope, and software support.
  • A new dataset is presented to demonstrate the practical implementation of these statistical techniques.

Conclusions:

  • The choice of method depends on research objectives and data characteristics.
  • Understanding these analytical strategies is crucial for robust interpretation of complex longitudinal studies.
  • Effective analysis of multivariate longitudinal data enhances insights in biomedical and public health research.