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

Censoring Survival Data01:09

Censoring Survival Data

687
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...
687
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

766
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
766
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

495
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.
495
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

994
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...
994
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

725
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...
725

You might also read

Related Articles

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

Sort by
Same author

Sulfur-Substituted SAMs Induce Pb─S Antibonding Hybridization for Efficient and Durable Perovskite-Silicon Tandems.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

Diagnostic efficacy of circulating tumor cell in clinically significant prostate cancer.

Scientific reports·2026
Same author

[Embryonic Stem Cell-Derived Mesenchymal Stromal Cell Exosomes Protect Against Radiation-Induced Lymphocyte Injury and Its Related Mechanisms].

Zhongguo shi yan xue ye xue za zhi·2026
Same author

HOPX is required for the generation of umbilical cord blood-derived memory-like NK cells induced by three cytokines.

Frontiers in immunology·2026
Same author

Research on the Determination Method of Additional Safety Factor Margin for Ultradeep Well Pipe Strings under Multisource Loads.

ACS omega·2026
Same author

Machine Learning Accelerated Non-Adiabatic Molecular Dynamics Elucidates Local Polarization Effects on Non-radiative Recombination in Halide Perovskites.

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

Related Experiment Video

Updated: Apr 16, 2026

Frailty Assessment in an Aging Mouse Model
06:58

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

898

Multiple frailty model for clustered interval-censored data with frailty selection.

Chun Pan1, Bo Cai2, Lianming Wang3

  • 11 Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA.

Statistical Methods in Medical Research
|March 22, 2015
PubMed
Summary

This study introduces a novel multiple frailty model for clustered interval-censored time-to-event data. It accounts for heterogeneity and predictor variations across clusters, enhancing survival analysis in complex medical studies.

Keywords:
Frailtyheterogeneity testinterval-censoredproportional hazards modelsemiparametric regression

More Related Videos

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
05:53

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

17.2K
Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles
04:00

Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles

Published on: July 26, 2024

1.6K

Related Experiment Videos

Last Updated: Apr 16, 2026

Frailty Assessment in an Aging Mouse Model
06:58

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

898
Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty
05:53

Measuring Frailty in HIV-infected Individuals. Identification of Frail Patients is the First Step to Amelioration and Reversal of Frailty

Published on: July 24, 2013

17.2K
Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles
04:00

Author Spotlight: Assessing Surgical Frailty with Point-of-Care Ultrasound of Quadriceps Muscles

Published on: July 26, 2024

1.6K

Area of Science:

  • Biostatistics
  • Survival Analysis
  • Epidemiology

Background:

  • Interval-censored time-to-event data arise when events are detected via periodic examinations.
  • Clustered data violate independence assumptions, common in multi-center trials.
  • Existing frailty models address heterogeneity but may not capture all complexities.

Purpose of the Study:

  • To propose a multiple frailty proportional hazards model for clustered interval-censored data.
  • To account for baseline heterogeneity and predictor effect variation across clusters.
  • To quantify the probabilities of frailty existence.

Main Methods:

  • Development of a multiple frailty proportional hazards model.
  • Application to interval-censored time-to-event data from clustered observations.
  • Statistical modeling to estimate frailty probabilities and predictor effects.

Main Results:

  • The proposed model effectively handles heterogeneity and predictor variations in clustered data.
  • Probabilities of frailty existence can be quantified.
  • Demonstrates utility in complex survival data scenarios.

Conclusions:

  • The multiple frailty model offers a robust approach for analyzing clustered interval-censored survival data.
  • Enhances understanding of heterogeneity and predictor effects in multi-center studies.
  • Applicable to HIV, infection, and oncology progression-free survival analyses.