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

894
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...
894
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

467
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.
467
Survival Curves01:18

Survival Curves

788
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...
788
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

674
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...
674
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

663
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,...
663

You might also read

Related Articles

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

Sort by
Same author

Addressing methodological and analytical pitfalls on medical devices related adverse events.

The Indian journal of medical research·2026
Same author

Correlation between DPP-4 Gene Expression and T-Cell Subset Marker Genes in Chronic Liver Disease Patients in Eastern India: An Observational Study.

Indian journal of endocrinology and metabolism·2026
Same author

Regulatory and Ethical Frameworks for First-in-Human Clinical Trials in India: Proceedings of a Codesigned Capacity-Building Workshop.

Clinical drug investigation·2026
Same author

Deferred cord clamping (DCC) at 1 min versus 3 min in preterm neonates - an open label randomized, controlled trial.

European journal of pediatrics·2026
Same author

Comparison of Bovine Lipid Extract Surfactant and Poractant Alfa Administered via LISA in Preterm Infants(28<sup>+0</sup> to 34<sup>+6</sup> Week) With Respiratory Distress Syndrome: A Randomized Controlled Trial.

Pediatric pulmonology·2025
Same author

Angular assessment of joints in juvenile idiopathic arthritis.

Rheumatology and immunology research·2025
Same journal

Severe Irritant Contact Dermatitis to <i>Cyperus Scariosus</i>: A Side Effect of Ayurvedic <i>Plava</i>.

Indian journal of dermatology·2026
Same journal

Extensive Grouped Papules on the Vulva in a Patient with Cervical Cancer.

Indian journal of dermatology·2026
Same journal

Facial Melanosis: A Comprehensive Review of Uncommon and Common Presentations with Personal Experience.

Indian journal of dermatology·2026
Same journal

Exploring AI as a Diagnostic Tool in Medical Imaging for Dermatopathological Diseases.

Indian journal of dermatology·2026
Same journal

Carotid Intima-Media Thickness and Retrobulbar Blood Flow in Patients with Psoriasis.

Indian journal of dermatology·2026
Same journal

Pyoderma Gangrenosum-Like Ulcer Secondary to Antiphospholipid Syndrome.

Indian journal of dermatology·2026
See all related articles

Related Experiment Video

Updated: Mar 1, 2026

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

10.9K

Biostatistics Series Module 9: Survival Analysis.

Avijit Hazra1, Nithya Gogtay2

  • 1Department of Pharmacology, Institute of Postgraduate Medical Education and Research, Kolkata, West Bengal, India.

Indian Journal of Dermatology
|June 7, 2017
PubMed
Summary
This summary is machine-generated.

Survival analysis is crucial for analyzing "time to event" data, especially with censored observations. It offers methods like Kaplan-Meier and Cox regression for robust insights without assuming normal distribution.

Keywords:
CensoringCox proportional hazard modelKaplan–Meier plotlog-rank testsurvival analysis

More Related Videos

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.9K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

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

Published on: July 22, 2016

8.9K

Related Experiment Videos

Last Updated: Mar 1, 2026

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

10.9K
Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.9K
Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

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

Published on: July 22, 2016

8.9K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Clinical Research

Background:

  • Survival analysis addresses "time to event" data, traditionally focusing on cancer mortality but applicable to any time-bound event.
  • Censored observations, where complete event data is unavailable for subjects (e.g., study withdrawal), are inherent challenges.
  • Standard statistical methods are unsuitable for censored data, necessitating specialized techniques.

Purpose of the Study:

  • To provide an overview of survival analysis methodologies.
  • To highlight the importance of handling censored observations appropriately.
  • To introduce key descriptive and inferential techniques in survival analysis.

Main Methods:

  • Descriptive methods: Life tables, Kaplan-Meier curves for visualizing survival probabilities.
  • Inferential methods: Log-rank test for comparing survival between groups.
  • Regression modeling: Cox proportional hazards model for analyzing multiple predictors of survival.

Main Results:

  • Kaplan-Meier plots are standard for visualizing survival probabilities over time.
  • Log-rank tests enable group comparisons and hazard ratio (HR) estimation.
  • Cox regression provides adjusted HRs for multiple factors influencing survival, accommodating various covariate types.

Conclusions:

  • Survival analysis methods are essential for accurately analyzing time-to-event data, particularly with censored observations.
  • Techniques like Kaplan-Meier and Cox regression offer robust analytical frameworks without assuming normal distributions.
  • These methods are vital for understanding event occurrences and risk factors in diverse research fields.