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 Research02:20

Longitudinal Research

13.5K
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...
13.5K
Longitudinal Studies01:26

Longitudinal Studies

554
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...
554
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

448
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.
448
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

862
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
862
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

636
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...
636
Observational Studies01:11

Observational Studies

11.2K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
11.2K

You might also read

Related Articles

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

Sort by
Same author

DASC-LOT Framework and S3 Metric: A Novel Evaluation and Benchmarking Method to Assess Initiation, Duration, and Spectrum of Antimicrobial Usage at Inpatient Hospital Settings.

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

Complete mitochondrial genome and microsatellite marker development of the Antarctic scallop (Adamussium colbecki) for its population genetics analysis.

PloS one·2026
Same author

Agentic Artificial Intelligence for the Automated Generation of Accurate Summary Podcasts of Radiology Research Papers.

Korean journal of radiology·2026
Same author

Heteroatom-Activated Cyclization: A Unified Route to Diverse Six-Membered <i>N</i>-Heterocycles from Homopropargylic Amines.

The Journal of organic chemistry·2026
Same author

Korean Society of Heart Failure Guidelines for the Palliative Care and Hospice for Heart Failure Patients.

International journal of heart failure·2026
Same author

Implant-displacement views alone for breast cancer screening in women with implants: a multicenter retrospective study.

European radiology·2026

Related Experiment Video

Updated: Feb 20, 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

6.8K

Comprehensive Analysis of Asynchronous Binary Variable Associations in Longitudinal End-of-Life Studies.

Zhuangzhuang Liu1, Sanghee Kim2, Hyunkeun Cho3

  • 1Oncology Development, AbbVie, Chicago, Illinois, USA.

Statistics in Medicine
|February 19, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for analyzing how two binary variables change together over time, especially with missing data. The approach was used to examine hypertension trends between mothers and daughters in the Framingham Heart Study.

Keywords:
asynchronous longitudinal databinary variablesend‐of‐life studykernel weightingnonparametric estimationtime‐varying odds ratio

More Related Videos

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

34.9K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K

Related Experiment Videos

Last Updated: Feb 20, 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

6.8K
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

34.9K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Epidemiology

Background:

  • Understanding dynamic relationships between binary variables over time is vital in biomedical research.
  • Existing methods face challenges with variables measured at different times and missing data.

Purpose of the Study:

  • To develop and validate novel statistical measures for longitudinal bivariate associations.
  • To address complexities of time-varying effects and missing data in longitudinal studies.
  • To apply the methodology to real-world data for insights into long-term health trends.

Main Methods:

  • Introduced bivariate time-varying odds ratio and relative risk measures.
  • Developed a nonparametric approach for longitudinal samples with varying measurement timelines.
  • Implemented a missing data model with inverse-probability weighting validated by simulations.

Main Results:

  • The nonparametric approach effectively handles concurrent and nonconcurrent sampling.
  • Inverse-probability weighting successfully corrected biases caused by missing data.
  • Analysis of the Framingham Heart Study revealed temporal changes in mother-daughter hypertension association over 45 years.

Conclusions:

  • The novel methodology provides robust tools for analyzing dynamic bivariate associations in longitudinal data.
  • The approach is versatile, applicable to various sampling schemes and effectively handles missing data.
  • The Framingham Heart Study application highlights the method's utility in understanding familial health trends over extended periods.