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

Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.4K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.4K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

766
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...
766
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

581
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...
581
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

603
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
603
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

407
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.
407
Cancer Survival Analysis01:21

Cancer Survival Analysis

658
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...
658

You might also read

Related Articles

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

Sort by
Same author

Effect of respiratory acid-base balance on rocuronium-induced neuromuscular blockade and sugammadex-induced reversal in rat phrenic nerve hemidiaphragm.

Anesthesia and pain medicine·2026
Same author

Interpreting relative risks and odds ratios after propensity score matching: practical guidance for clinical research.

Korean journal of anesthesiology·2026
Same author

Impact of treatment goals on outcomes in critically ill nonagenarians: a retrospective observational study.

BMC geriatrics·2026
Same author

Percolation-Limited Threshold Switching in Strain-Graded Mott Devices.

ACS nano·2025
Same author

Obstructive Sleep Apnea and Postoperative Cognitive Decline in Non-Cardiac Surgery: A Prospective Cohort Study.

Brain and behavior·2025
Same author

Hybrid MBE Route to Adsorption-Controlled Growth of BaTiO<sub>3</sub> Membranes with Robust Polarization Switching.

Nano letters·2025

Related Experiment Video

Updated: Jan 24, 2026

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.9K

Survival analysis: part II - applied clinical data analysis.

Junyong In1, Dong Kyu Lee2

  • 1Department of Anesthesiology and Pain Medicine, Dongguk University Ilsan Hospital, Goyang, Korea.

Korean Journal of Anesthesiology
|May 18, 2019
PubMed
Summary

This review details survival analysis methods, including proportional hazard assumption validation and Cox regression models. It provides practical codes and interpretations for reliable scientific results using personal data.

Keywords:
Cox regressionExtended Cox regressionGoodness of fit testLog minus log plotProportional hazard assumptionSchoenfeld residualStratified Cox regression

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K
Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

10.2K

Related Experiment Videos

Last Updated: Jan 24, 2026

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
07:12

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

9.9K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.3K
Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia
10:05

Measurement & Analysis of the Temporal Discrimination Threshold Applied to Cervical Dystonia

Published on: January 27, 2018

10.2K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Statistics

Background:

  • Survival analysis is a critical statistical method for analyzing time-to-event data.
  • Understanding and applying survival analysis techniques are essential for drawing valid conclusions from research.
  • Previous work established foundational concepts, necessitating a deeper dive into advanced methods.

Purpose of the Study:

  • To provide in-depth concepts and practical codes for survival analysis.
  • To explain the validation of the proportional hazard assumption using graphical methods and goodness-of-fit tests.
  • To introduce extended Cox regression models for violated assumptions, including stratified and time-dependent models.

Main Methods:

  • Review of survival analysis concepts and statistical methodologies.
  • Inclusion of detailed source code for practical application using statistical packages.
  • Explanation of graphical analysis and goodness-of-fit tests for assumption validation.

Main Results:

  • Demonstration of survival analysis techniques with applicable codes and examples.
  • Guidance on validating the proportional hazard assumption, a key component of survival analysis.
  • Introduction to advanced models like stratified Cox regression and time-dependent Cox regression.

Conclusions:

  • Proper validation of assumptions and understanding of advanced models enhance statistical power.
  • Survival analysis, when applied correctly with appropriate methods, yields reliable scientific results.
  • The provided codes and interpretations facilitate the practical realization of survival analysis on personal data.