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

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,...

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...

Cancer Survival Analysis

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...

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...

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

EMPIRE (NSABP FC-13): a biomarker-driven phase II platform trial evaluating cemiplimab-based immunotherapy in microsatellite-stable colorectal cancer with ctDNA-defined minimal residual disease.

Future oncology (London, England)·2026

Same author

The Late Quaternary climate impact on the genome of the woodland strawberry (Fragaria vesca), a perennial herb.

Communications biology·2026

Same author

Analyses of adverse events and patient-reported outcomes with the toxicity index in cancer clinical trials.

Cancer·2025

Same author

The Definition of Cure in Colon Cancer: A Pooled Analysis of 15 Randomized Clinical Trials.

JAMA oncology·2025

Same author

Erratum: Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2025

Same author

NSABP FC-11: A phase II study of neratinib plus trastuzumab or neratinib plus cetuximab in patients with "quadruple wild-type" (KRAS/NRAS/BRAF/PIK3CA) metastatic colorectal cancer based on HER2 status: amplified, non-amplified (wild-type), or mutated.

Cancer chemotherapy and pharmacology·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

Using cure models and multiple imputation to utilize recurrence as an auxiliary variable for overall survival.

Anna S C Conlon¹, Jeremy M G Taylor, Daniel J Sargent

¹Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA.

Clinical Trials (London, England)

|September 17, 2011

Summary

Utilizing recurrence and death models with multiple imputation in cancer trials can enhance treatment effect estimation efficiency. This approach offers potential for shortening clinical trial durations by recovering information lost due to censoring.

More Related Videos

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

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

Published on: December 9, 2015

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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

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

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

Published on: December 9, 2015

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

Biostatistics
Clinical Trials
Cancer Research

Background:

Intermediate outcome variables, such as time to recurrence in cancer, can serve as auxiliary markers for overall survival.
These markers offer valuable auxiliary information for analyzing survival times in randomized clinical trials.

Purpose of the Study:

To assess if models linking recurrence and death, coupled with multiple imputation for censored data, improve treatment effect estimation efficiency.
To determine if this methodology can contribute to shortening clinical trial lengths.

Main Methods:

Modeling recurrence and death times using data from 12 colorectal cancer trials.
Employing multiple imputation to handle missing data from censoring, utilizing a cure model for recurrence and a time-dependent Weibull model for death.
Imputing recurrence times, then death times conditionally, and analyzing using log-rank tests and Cox models after artificial censoring.

Main Results:

Demonstrated modest but consistent efficiency gains by incorporating auxiliary recurrence time information.
Treatment effect estimates from imputed data fell between original and artificially censored data, indicating successful recovery of censored information.
The imputation procedure effectively recovered some information lost due to censoring.

Conclusions:

The proposed parametric models, while requiring distributional assumptions, show promise for enhancing treatment effect estimation efficiency in cancer trials.
This methodology may lead to shorter clinical trial durations by maximizing the use of available data.