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

269
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...
269
Longitudinal Research02:20

Longitudinal Research

12.6K
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...
12.6K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

425
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...
425
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

327
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...
327
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

587
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:
587
Actuarial Approach01:20

Actuarial Approach

143
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
143

You might also read

Related Articles

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

Sort by
Same author

Consistency of c-Met protein overexpression over time in patients with non-squamous non-small cell lung cancer.

Histopathology·2026
Same author

Explainable machine learning in healthcare: methods, interpretation, and applications for clinical research.

Journal of the American Medical Informatics Association : JAMIA·2026
Same author

Prospective real-world evaluation of t(11;14) prevalence and disease biology in multiple myeloma: MEDICI study analysis.

Blood advances·2025
Same author

Nonparametric analysis of delayed treatment effects using single-crossing constraints.

Biometrical journal. Biometrische Zeitschrift·2024
Same author

Efficacy and anti-inflammatory analysis of glucocorticoid, antihistamine and leukotriene receptor antagonist in the treatment of allergic rhinitis.

World journal of clinical cases·2023
Same author

AIDE: Adaptive intrapatient dose escalation designs to accelerate Phase I clinical trials.

Pharmaceutical statistics·2022
Same journal

The sequential cohort randomized clinical trial: A study design for community-engaged clinical research in a rural American Indian community.

Contemporary clinical trials·2026
Same journal

Rationale, design, and statistical analysis plan for a randomized, double-blind, placebo-controlled trial of Limosilactobacillus reuteri to support mother-infant bonding and maternal socioemotional functioning in postpartum women at increased risk for postpartum depression.

Contemporary clinical trials·2026
Same journal

Effectiveness of clinical trial recruitment strategies in a safety-net hospital.

Contemporary clinical trials·2026
Same journal

Are older adult research participants representative of the general population? Results from 19 clinical studies at one academic research center.

Contemporary clinical trials·2026
Same journal

Multi-site feasibility of a web-based and telephone navigation intervention to connect lung cancer caregivers in community oncology settings with resources: Protocol for the WF-2301CD Caregiver Oncology Needs Evaluation Tool (CONNECT) trial.

Contemporary clinical trials·2026
Same journal

Rationale and design of a CArdiac rehabilitation program on the prevention of CardioTOXicity in breast Cancer patients undergoing treatment with anthracyclines and/or trastuzumab (CARPTOX-BC) trial: A randomized, active control group, open-label trial.

Contemporary clinical trials·2026
See all related articles

Related Experiment Video

Updated: Oct 1, 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

6.4K

Analyzing longitudinal binary data in clinical studies.

Yihan Li1, Dai Feng1, Yunxia Sui1

  • 1Data and Statistical Sciences, AbbVie Inc., 1 North Waukegan Road, North Chicago, IL 60064, USA.

Contemporary Clinical Trials
|March 3, 2022
PubMed
Summary
This summary is machine-generated.

For repeated binary outcomes in clinical trials, Generalized Linear Mixed Models (GLMM) are preferred over Generalized Estimating Equations (GEE) under the Missing at Random assumption. A Multiple Imputation and GEE approach is recommended for complex missing data scenarios.

Keywords:
Binary endpointsGEEGLMMLongitudinal data analysisMARMultiple imputation

More Related Videos

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

766
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

14.7K

Related Experiment Videos

Last Updated: Oct 1, 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

6.4K
Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment
08:36

Author Spotlight: Evaluating the Adjuvant Efficacy and Safety of Angong Niuhuang Pill in Viral Encephalitis Treatment

Published on: April 19, 2024

766
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

14.7K

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Longitudinal Data Analysis

Background:

  • Clinical studies frequently involve binary outcomes measured repeatedly over time.
  • Analyzing such longitudinal binary data requires robust statistical methodologies.
  • Generalized Linear Mixed Models (GLMM) and Generalized Estimating Equations (GEE) are common approaches.

Purpose of the Study:

  • To review and evaluate Generalized Linear Mixed Models (GLMM) and Generalized Estimating Equations (GEE) for analyzing repeated binary outcomes in clinical studies.
  • To provide recommendations for selecting and implementing statistical models, particularly addressing missing data.
  • To compare the performance of GLMM and GEE under realistic clinical trial conditions via simulation.

Main Methods:

  • Comprehensive literature review of statistical methods for longitudinal binary data.
  • Simulation study evaluating GLMM and GEE performance in typical clinical trial settings.
  • Focus on scenarios with missing data under the Missing at Random (MAR) assumption.

Main Results:

  • Generalized Linear Mixed Models (GLMM) demonstrate superior performance compared to Generalized Estimating Equations (GEE) when data are Missing at Random (MAR).
  • SAS PROC GLIMMIX is recommended for implementing GLMM in clinical trial data analysis.
  • A two-step approach combining Multiple Imputation (MI) with GEE (MI-GEE) is advised for high or unbalanced missing proportions, especially with underlying continuous variables.

Conclusions:

  • GLMM is the preferred method for analyzing repeated binary outcomes in clinical studies under MAR assumptions.
  • The choice of statistical method should consider the nature of missing data and underlying data structure.
  • Specific implementation recommendations (SAS PROC GLIMMIX, MI-GEE) are provided to guide practitioners.