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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

370
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.
370
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

695
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...
695
Biostatistics: Overview01:20

Biostatistics: Overview

681
Biostatistics plays a crucial role in understanding and analyzing data in healthcare and biology. Biostatisticians conduct experiments, gather evidence, and draw meaningful conclusions using statistical methods and techniques. Different variables form the foundation of biostatistical analysis, allowing researchers to understand and interpret data effectively. These variables are classified into different types, each serving a specific purpose in statistical analysis.
Discrete variables are...
681
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

965
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
965
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

524
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...
524
Group Design02:01

Group Design

10.1K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
10.1K

You might also read

Related Articles

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

Sort by
Same author

Differential analysis of CACNA1C polymorphisms among patients with depression and bipolar disorder and exploration of sleep as a mediator.

Journal of affective disorders·2026
Same author

Prediction of pedicle screw fixation strength under craniocaudal cyclic load: comparison of various models trained on quantitative CT based finite element analysis.

European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society·2026
Same author

A metal-organic framework-based trilayer biomimetic coating on lacrimal stents for dual-drug therapy to prevent restenosis.

Acta biomaterialia·2026
Same author

Comparison of myopia control efficacy between single vision and highly aspherical Lenslet spectacle lenses in children with low myopia: a 1-year retrospective study.

Frontiers in medicine·2026
Same author

Giant Switchable Remanent Polarization and Photocurrent in Ferroelectric Thin Film Photomemristor for In Situ Training.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Case Report: A rare case of myopic macular pit mimicking duplication of the optic disc.

Frontiers in medicine·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

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

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Jan 6, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.6K

Sequential adaptive variables and subject selection for GEE methods.

Zimu Chen1, Zhanfeng Wang1, Yuan-Chin Ivan Chang2

  • 1International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, China.

Biometrics
|October 11, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive sampling method to efficiently model correlated data, accelerating data collection for epidemiological studies. The new approach integrates variable selection for improved analysis of complex datasets.

Keywords:
active learningadaptive samplinggeneralized estimating equationssequential estimationstopping time

More Related Videos

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Related Experiment Videos

Last Updated: Jan 6, 2026

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.6K
The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

6.3K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K

Area of Science:

  • Statistics
  • Machine Learning
  • Epidemiology

Background:

  • Modeling correlated or highly stratified multiple-response data is crucial in large-scale studies.
  • Generalized estimating equations (GEE) are widely used but can be inefficient with large datasets.
  • Efficient data collection is vital due to the time-consuming nature of gathering stratified or correlated response data.

Purpose of the Study:

  • To develop and evaluate an adaptive sampling procedure for modeling correlated response data.
  • To integrate variable selection and adaptive sampling for enhanced efficiency in data analysis.
  • To demonstrate the utility of the proposed method using both simulated and real-world data.

Main Methods:

  • A sequential procedure integrating adaptive sampling and variable selection was developed.
  • The method aims to accelerate the data collection process for correlated response data.
  • Statistical properties of the procedure were analyzed, and performance was validated on synthesized and real datasets.

Main Results:

  • The proposed procedure effectively models correlated response data.
  • Adaptive sampling and variable selection features enhance the efficiency of data analysis.
  • The method demonstrates practical usefulness in analyzing complex epidemiological and cohort study data.

Conclusions:

  • The integrated adaptive sampling and variable selection procedure offers a beneficial approach for modeling correlated data.
  • This method can significantly accelerate data collection and analysis in large epidemiological and multisite cohort studies.
  • The developed technique provides a valuable tool for researchers dealing with complex, high-dimensional response data.