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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

729
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...
729
Cancer Survival Analysis01:21

Cancer Survival Analysis

857
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...
857
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

1.0K
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...
1.0K
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

503
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.
503
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.3K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
7.3K

You might also read

Related Articles

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

Sort by
Same author

Recent Advances in Umami Peptides From Livestock and Poultry By-Products: Influencing Factors, Structural Modification, Off-Flavor Control, and Bitterness Masking.

Comprehensive reviews in food science and food safety·2026
Same author

Non-fused ring A-D-A-type dyes for near-infrared II fluorescence imaging guided phototherapy and immune activation of tumour.

Biochemical and biophysical research communications·2026
Same author

Zinc Inhibits BTK Phosphorylation in Macrophages to Ameliorate Encephalitis in Neurotropic Virus-Infected Mice.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same author

[Clinical and genetic analysis of a Chinese pedigree affected with MRXS34 syndrome due to variant of NONO gene].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics·2026
Same author

C-type lectins in arthropods: Evolutionary diversification and prospects for pathogen defense.

Journal of invertebrate pathology·2026
Same author

<i>Bifidobacterium animalis</i> subsp. <i>lactis</i> 832 Alleviates DSS-Induced Colitis in a Murine Model by Regulating Gut Microbiota and Phospholipid Metabolism.

Microorganisms·2026

Related Experiment Video

Updated: Apr 20, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.9K

Survival analysis tools in genomics research.

Xintong Chen1, Xiaochen Sun2, Yujin Hoshida3

  • 1Liver Cancer Program, Tisch Cancer Institute, Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Box 1123, New York, NY, 10029, USA. xintong.chen@mssm.edu.

Human Genomics
|November 26, 2014
PubMed
Summary
This summary is machine-generated.

Genomic data analysis using survival analysis links molecular findings to clinical outcomes. This review covers tools for survival analysis in genomics research and discusses clinical utility assessment.

More Related Videos

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
09:33

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

1.9K
Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
04:58

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

4.9K

Related Experiment Videos

Last Updated: Apr 20, 2026

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.9K
Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
09:33

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

1.9K
Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance
04:58

Author Spotlight: Investigating the Role of Repetitive DNA Misregulation in Cancer Initiation and Immunotherapy Resistance

Published on: December 13, 2024

4.9K

Area of Science:

  • Genomics
  • Biomedical Research
  • Bioinformatics

Background:

  • Growing need to connect experimental molecular findings with clinical relevance.
  • Survival analysis methods are increasingly applied to genomics data.
  • Genome-wide profiles are crucial for identifying biomarkers and therapeutic targets.

Purpose of the Study:

  • To review existing software, web applications, and databases for survival analysis in genomics.
  • To discuss challenges in evaluating the clinical utility of genomic profiling-derived features.

Main Methods:

  • Literature review of bioinformatics tools and databases.
  • Analysis of methodologies for survival analysis in genomics.
  • Discussion of clinical utility assessment frameworks.

Main Results:

  • A comprehensive overview of available resources for genomics survival analysis.
  • Identification of key considerations for assessing clinical utility.
  • Highlighting the gap between genomic discoveries and clinical application.

Conclusions:

  • Effective tools exist for survival analysis in genomics research.
  • Rigorous assessment of clinical utility is essential for translating genomic findings.
  • Further development is needed to bridge the gap between molecular profiling and patient outcomes.