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

157
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...
157
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

84
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.
84
Survival Tree01:19

Survival Tree

50
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
50
Survival Curves01:18

Survival Curves

88
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...
88
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

117
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...
117
Cancer Survival Analysis01:21

Cancer Survival Analysis

319
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...
319

You might also read

Related Articles

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

Sort by
Same author

MT<sup>2</sup>-CSD and LLM-CRAN: A new dataset and an LLM-based multi-semantic knowledge fusion model for conversational stance detection.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Ultrasound reliance and landmark-based proficiency in vascular access training: a multicenter cross-sectional survey of anesthesia trainees and teaching faculty in Eastern China.

BMC medical education·2026
Same author

Development and application of an ANN-perception-based autonomous control system for <i>Escherichia coli</i> cultivation process.

Frontiers in microbiology·2026
Same author

Robust Deep Active Learning via Distance-Measured Data Mixing and Adversarial Training.

Entropy (Basel, Switzerland)·2025
Same author

High-yield astaxanthin production process development and scale-up validation from wild-type <i>Phaffia rhodozyma</i> via parameter optimization and LSTM modeling.

Frontiers in microbiology·2025
Same author

Pressure Induced Molecular-Arrangement and Charge-Density Perturbance in Doped Polymer for Intelligent Motion and Vocal Recognitions.

Advanced materials (Deerfield Beach, Fla.)·2025

Related Experiment Video

Updated: May 24, 2025

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.1K

Interactive Explainable Deep Survival Analysis.

Lu Wang, Xinyu Qin, Jingyan Jiang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a trustworthy and efficient algorithm for survival analysis, crucial for predicting patient outcomes and improving healthcare decisions. The developed method enhances time-to-event predictions in precision medicine and clinical support.

    More Related Videos

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.1K
    Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    938

    Related Experiment Videos

    Last Updated: May 24, 2025

    Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
    07:02

    Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

    Published on: January 19, 2019

    6.1K
    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

    2.1K
    Author Spotlight: Advancing Alzheimer's Research &#8211; Exploring Early Detection and Multi-Omics Approaches
    09:47

    Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

    Published on: December 15, 2023

    938

    Area of Science:

    • Biostatistics
    • Health Informatics
    • Machine Learning in Healthcare

    Background:

    • Survival analysis is vital for healthcare, aiding in disease progression modeling, prognostic factor identification, and risk assessment.
    • Accurate, interpretable, and trustworthy survival models are essential for clinical adoption and decision-making.
    • Human-AI interaction and clear model interpretation are key to enhancing model performance and user trust.

    Purpose of the Study:

    • To develop a novel algorithm and methodology for trustworthy and time-efficient data-driven decision-making.
    • To support the implementation of survival analysis in healthcare for prevention and early intervention strategies.
    • To enhance the usability and reliability of survival models for healthcare providers.

    Main Methods:

    • Development of a new algorithm for survival analysis.
    • Implementation of methods to ensure trustworthiness and time-efficiency in model predictions.
    • Validation using a public cancer dataset to assess prediction accuracy.

    Main Results:

    • The developed algorithm demonstrated efficiency in predicting cancer patient survival times.
    • Experimental results confirmed the algorithm's capability on a public cancer dataset.
    • The study validates the proposed approach for practical healthcare applications.

    Conclusions:

    • The developed algorithm and methods facilitate trustworthy and time-efficient survival analysis.
    • This approach supports data-driven decision-making for disease prevention and early intervention.
    • The findings highlight the potential of the algorithm to improve survival time predictions in oncology.