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

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...

Survival Curves

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...

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.

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

Development of a health subindex for genetic selection of bulls and cows in Canadian dairy operations.

JDS communications·2026

Same author

Prognostic impact of focal lesion location and persistence in multiple myeloma: insights from serial PET/DWI imaging.

Blood advances·2025

Same author

Altered mesenchymal and endothelial subsets in interstitial bone marrow and focal lesions in myeloma patients and SCID-hu mice.

Haematologica·2025

Same author

Long-Term Follow-Up of Patients With Multiple Myeloma Treated on Earlier Total Therapy Protocols: A Secondary Analysis of 3 Clinical Trials.

JAMA oncology·2025

Same author

Long-term follow-up of Total Therapy IV: a phase 3 clinical trial for standard-risk multiple myeloma.

Blood advances·2025

Same author

Second primary malignancies after tandem autologous hematopoietic stem cell transplantation for multiple myeloma.

American journal of hematology·2024

Search research articles

Related Experiment Video

Updated: May 21, 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

Cure models as a useful statistical tool for analyzing survival.

Megan Othus¹, Bart Barlogie, Michael L Leblanc

¹Fred Hutchinson Cancer Research Center, Seattle, WA 98117, USA. mothus@fhcrc.org

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research

|June 8, 2012

Summary

Cure models offer new insights into cancer survival, particularly for multiple myeloma. This study demonstrates their utility in identifying potential long-term survivors, challenging the disease's incurable status.

More Related Videos

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

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Related Experiment Videos

Last Updated: May 21, 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

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

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Biostatistics
Oncology
Survival Analysis

Background:

Cure models are established in statistics but underutilized in clinical research.
Cancer patients can achieve long-term survival, necessitating advanced analytical tools.
Standard survival models may not fully capture long-term outcomes in certain cancers.

Purpose of the Study:

To introduce cure models to the clinical literature.
To explain the application and benefits of cure models.
To analyze multiple myeloma survival trends using cure models.

Main Methods:

Review of cure model principles.
Application of cure models to multiple myeloma patient data.
Comparison with traditional Cox proportional hazards models.

Main Results:

Cure models provide a framework to assess long-term survival in multiple myeloma.
Analysis indicates potential for a subset of patients to become long-term survivors.
Demonstrates the value of cure models over standard methods for this specific outcome.

Conclusions:

Cure models are valuable for analyzing cancer survival data, especially for potentially curable diseases.
Findings suggest that specific therapies may induce long-term survival in a proportion of multiple myeloma patients.
Further research using cure models can refine understanding of cancer survivorship and treatment efficacy.