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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

311
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
311
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

468
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.
468
Censoring Survival Data01:09

Censoring Survival Data

612
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
612
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

674
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...
674
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

894
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...
894

You might also read

Related Articles

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

Sort by
Same author

Healthcare system cost evaluation of antiviral stockpiling for pandemic influenza preparedness.

Biosecurity and bioterrorism : biodefense strategy, practice, and science·2010
Same author

PDGF-CC blockade inhibits pathological angiogenesis by acting on multiple cellular and molecular targets.

Proceedings of the National Academy of Sciences of the United States of America·2010
Same author

Betulin induces mitochondrial cytochrome c release associated apoptosis in human cancer cells.

Molecular carcinogenesis·2010
Same author

Development of novel 5-fluorouracil carrier erythrocyte with pharmacokinetics and potent antitumor activity in mice bearing malignant ascites.

Journal of gastroenterology and hepatology·2010
Same author

[Effectiveness of educational interventions in children with chronic diseases and their parents].

Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics·2010
Same author

Genomic identification of a novel mutation in hfq that provides multiple benefits in evolving glucose-limited populations of Escherichia coli.

Journal of bacteriology·2010

Related Experiment Video

Updated: Mar 2, 2026

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

3.8K

Semiparametric Random Effects Models for Longitudinal Data with Informative Observation Times.

Yang Li1, Yanqing Sun1

  • 1Department of Mathematics and Statistics, UNC Charlotte, Charlotte, NC 28223.

Statistics and Its Interface
|May 19, 2017
PubMed
Summary

This study introduces a new joint analysis for longitudinal data, accounting for correlations between responses and observation times. The method uses time-dependent random effects for more accurate statistical modeling in medical follow-up studies.

Keywords:
estimating equationsinformative censoringinformative observation processjoint analysis approachlongitudinal data

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
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

Related Experiment Videos

Last Updated: Mar 2, 2026

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

3.8K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K
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

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Medical Statistics

Background:

  • Longitudinal data are common in medical follow-up studies, with responses recorded at discrete times.
  • Existing methods often assume observation times are independent of response processes.
  • This independence assumption can limit the accuracy of longitudinal data analysis.

Purpose of the Study:

  • To develop a joint analysis approach for longitudinal data that accounts for correlations between responses and observation/follow-up times.
  • To address limitations of current methods that assume independence between observation times and response processes.
  • To provide a more robust statistical framework for analyzing complex longitudinal datasets.

Main Methods:

  • A joint analysis model is proposed incorporating time-dependent random effects.
  • These random effects capture potential correlations among responses and observation/follow-up times.
  • Estimating equations are derived for parameter estimation.

Main Results:

  • The proposed method provides consistent and asymptotically normal parameter estimates.
  • A simulation study demonstrates the finite sample performance of the approach.
  • The method was successfully applied to real-world data from a skin cancer study.

Conclusions:

  • The joint analysis approach effectively models correlations between longitudinal responses and observation times.
  • This method offers an improved statistical framework for analyzing medical follow-up data.
  • The approach is validated through simulation and practical application, showing its utility and reliability.