Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Assumptions of Survival Analysis

Assumptions of Survival Analysis

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.

You might also read

Related Articles

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

Sort by

Same author

Model-based clustering of multiple images incorporating covariates.

Statistical methods in medical research·2026

Same author

Incidence of hidradenitis suppurativa in transgender and cisgender individuals: A multicenter matched cohort study.

Journal of the American Academy of Dermatology·2026

Same author

Acne Incidence and Severity in Transgender Individuals.

JAMA dermatology·2026

Same author

Assessing intra- and inter-method agreement of functional data.

Statistical methods in medical research·2023

Same author

An integrative latent class model of heterogeneous data modalities for diagnosing kidney obstruction.

Biostatistics (Oxford, England)·2023

Same author

Special issue dedicated to David Oakes.

Lifetime data analysis·2022

Search research articles

Related Experiment Video

Updated: Jun 11, 2026

Frailty Assessment in an Aging Mouse Model

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

A note on assessing agreement for frailty models.

Ying Guo¹, Amita K Manatunga

¹Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA.

Statistics & Probability Letters

|July 8, 2010

Summary

This summary is machine-generated.

This study explores agreement in biomedical sciences using frailty models for survival data. We developed a time-dependent concordance correlation coefficient (CCC) to assess agreement over time.

More Related Videos

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

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

Identifying Frailty Using Point-of-Care Ultrasonography: Image Acquisition and Assessment

Identifying Frailty Using Point-of-Care Ultrasonography: Image Acquisition and Assessment

Published on: July 26, 2024

Related Experiment Videos

Last Updated: Jun 11, 2026

Frailty Assessment in an Aging Mouse Model

Frailty Assessment in an Aging Mouse Model

Published on: September 23, 2025

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

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

Identifying Frailty Using Point-of-Care Ultrasonography: Image Acquisition and Assessment

Identifying Frailty Using Point-of-Care Ultrasonography: Image Acquisition and Assessment

Published on: July 26, 2024

Area of Science:

Biomedical Sciences
Statistics
Survival Analysis

Background:

Assessing agreement between measurements is crucial in biomedical research.
Frailty models are commonly used for correlated survival outcomes.

Purpose of the Study:

Investigate the agreement structure in frailty models for correlated survival outcomes.
Develop and analyze a time-dependent concordance correlation coefficient (CCC).

Main Methods:

Utilized frailty models with Weibull baseline hazards for bivariate survival times.
Derived analytic expressions for the CCC under various frailty distributions.
Developed a time-dependent CCC to measure agreement beyond a specific time point.

Main Results:

Presented analytic CCC expressions for common frailty distributions.
Characterized the temporal patterns of the time-dependent CCC.
Demonstrated how frailty models influence agreement structures.

Conclusions:

The study enhances understanding of agreement in correlated survival data.
The time-dependent CCC offers a novel tool for longitudinal agreement assessment.
Results aid in selecting appropriate frailty models based on agreement characteristics.