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

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

474
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...
474
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

380
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...
380
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

233
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.
233
Cancer Survival Analysis01:21

Cancer Survival Analysis

501
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...
501
Actuarial Approach01:20

Actuarial Approach

176
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
176
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

349
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,...
349

You might also read

Related Articles

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

Sort by
Same author

Pembrolizumab with or without chemotherapy among older adults with advanced lung adenocarcinoma: a national, nonrandomized open-label phase II trial (Alliance A171901).

Journal of the National Cancer Institute·2026
Same author

A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).

Journal of the National Medical Association·2026
Same author

Nationwide Mammographic Screening Among a Large Population of Underserved Subgroups.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Pediatric asthma management via integration of a remote spirometry device into an EHR-based artificial intelligence-powered clinical decision support system: A feasibility pragmatic clinical trial.

Contemporary clinical trials·2025
Same author

Association of age and performance status with adverse events in older adults with diffuse large B-cell lymphoma receiving frontline R-CHOP therapy: Alliance 151930, a secondary analysis of the phase III trial CALGB 50303.

Journal of geriatric oncology·2025
Same author

Deficit accumulation frailty index and treatment tolerability in AML: data from CALGB 11001 and 11002 (Alliance).

Blood advances·2024
Same journal

Obesity Promotes Lung Carcinogenesis Through Airway Immune Dysfunction.

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2026
Same journal

Corrigendum to 'The Ninth Edition Staging Project Publications' [Journal of Thoracic Oncology (2022-2024)].

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2026
Same journal

Brief Report: Aumolertinib as a Switch Therapy in Osimertinib-Intolerant NSCLC: The ACTIVE Trial.

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2026
Same journal

Brief Report: Proposed clinical trial guidelines based on clinicopathological heterogeneity of metastatic pulmonary large cell neuroendocrine carcinoma (LCNEC).

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2026
Same journal

Corrigendum to 'Patient-Reported Outcomes With Consolidation Durvalumab Versus Placebo After Concurrent Chemoradiotherapy in Limited-Stage SCLC: Results From the Phase 3 ADRIATIC Trial' [Journal of Thoracic Oncology volume 21 issue 6 (2026) 103564].

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2026
Same journal

ctDNA based MRD detection in stage III NSCLC treated with chemoradiotherapy and durvalumab.

Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer·2026
See all related articles

Related Experiment Video

Updated: Nov 8, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.5K

Time-To-Event Data: An Overview and Analysis Considerations.

Jennifer Le-Rademacher1, Xiaofei Wang2

  • 1Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota.

Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer
|April 22, 2021
PubMed
Summary
This summary is machine-generated.

This article explains time-to-event analysis for oncology researchers, focusing on overall survival and progression-free survival. Understanding these methods ensures accurate interpretation of cancer treatment efficacy data.

Keywords:
Competing risksCox modelKaplan-Meier estimatesLog-rank testSurvival analysisTime-to-event data

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.8K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.2K

Related Experiment Videos

Last Updated: Nov 8, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.5K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.8K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

9.2K

Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Research

Background:

  • Overall survival (OS) and progression-free survival (PFS) are key time-to-event endpoints in oncology for assessing treatment efficacy.
  • Time-to-event data analysis requires a robust statistical framework with specific assumptions for valid results.

Purpose of the Study:

  • To provide clinicians and lung cancer researchers with an overview of time-to-event data analysis.
  • To explain the fundamental mechanics of common analysis methods and potential challenges.
  • To guide the appropriate selection and interpretation of time-to-event analysis techniques.

Main Methods:

  • Overview of time-to-event data characteristics.
  • Explanation of fundamental statistical methods for survival analysis.
  • Discussion of common issues and assumptions in time-to-event data analysis.

Main Results:

  • Clinicians and researchers need to understand the principles of time-to-event analysis for accurate interpretation of oncology trial outcomes.
  • Knowledge of common methods and potential pitfalls is crucial for valid results.
  • Appropriate statistical methods ensure reliable assessment of treatment efficacy.

Conclusions:

  • Accurate interpretation of cancer treatment efficacy relies on appropriate time-to-event analysis.
  • Researchers should consult with statisticians specializing in survival analysis for complex studies.
  • Ensuring correct data collection and analysis methods is vital for robust oncology research.