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

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

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...

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.

Censoring Survival Data

Censoring Survival Data

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 reasons...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

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 Cox...

Kaplan-Meier Approach

Kaplan-Meier Approach

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,...

Introduction To Survival Analysis

Introduction To Survival Analysis

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 until a...

You might also read

Related Articles

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

Sort by

Same author

Specification of Estimands for Complex Disease Processes Using Multistate Models and Utility Functions.

Statistics in medicine·2025

Same author

The Use of Real-World Data for Studies of Dynamic Disease Processes.

The Journal of rheumatology·2025

Same author

Detecting latent interaction effects when analyzing binary traits.

PLoS genetics·2025

Same author

Methodological challenges in studying disease processes using observational cohort data.

Japanese journal of statistics and data science·2025

Same author

A genome-wide SNP-SNP interaction analysis exploring novel interacting loci associated with the risk of recurrence in colorectal cancer.

PloS one·2025

Same author

Correction: Canadian COVID-19 host genetics cohort replicates known severity associations.

PLoS genetics·2025

Search research articles

Related Experiment Video

Updated: May 29, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Semiparametric estimation in copula models for bivariate sequential survival times.

Jerald F Lawless¹, Yildiz E Yilmaz

¹Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada N2L 3G1.

Biometrical Journal. Biometrische Zeitschrift

|September 3, 2011

Summary

Analyzing sequential survival data is challenging due to dependent censoring and non-identifiable distributions. This study introduces a robust semiparametric method using copula functions to model joint survival times, improving analysis for complex biomedical studies.

More Related Videos

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

Related Experiment Videos

Last Updated: May 29, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

Area of Science:

Biostatistics
Survival Analysis
Medical Research

Background:

Sequential survival times present analytical challenges, including induced dependent censoring and non-identifiable marginal distributions for later events.
Existing fully parametric models offer solutions but raise concerns regarding robustness.
A significant proportion of individuals may not experience the first event, complicating standard survival analyses.

Purpose of the Study:

To introduce a novel semiparametric approach for analyzing sequentially observed survival times.
To address issues of dependent censoring and non-identifiability in survival data.
To provide more robust statistical estimates and enable checks on parametric model fit.

Main Methods:

Modeling the joint distribution of successive survival times using copula functions.
Developing semiparametric estimation procedures for copula parameters without assuming parametric marginal distributions.
Applying the methodology to survival data from colon cancer treatment studies.

Main Results:

The proposed copula-based method offers a robust alternative to traditional survival analysis techniques.
Semiparametric estimation enhances reliability by avoiding strict parametric assumptions on marginal distributions.
The approach successfully handles dependent censoring and non-identifiability inherent in sequential survival data.

Conclusions:

The copula-based semiparametric approach provides a flexible and robust framework for analyzing complex sequential survival data.
This methodology improves the estimation of survival distributions in the presence of censoring and non-identifiability.
The study demonstrates the practical utility of the approach in a real-world clinical setting, such as colon cancer survival analysis.