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

762
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...
762
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

573
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...
573
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

589
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...
589
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

405
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.
405
Cancer Survival Analysis01:21

Cancer Survival Analysis

652
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...
652
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.0K
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...
1.0K

You might also read

Related Articles

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

Sort by
Same author

The Relevance of Curative-Intent Metastasectomy in Colorectal Cancer Patients: Real-World Insights From a Certified Comprehensive Cancer Center in Germany.

International journal of cancer·2026
Same author

Development and Release of the Munich UICC Staging Tool (MUST): Advancing UICC Staging in Real-World Data With Insights From Pancreatic and Stomach Cancer.

International journal of cancer·2026
Same author

A Mobile Exhibition to Advance Cancer Prevention Awareness: A Cross-Sectional Evaluation Using the RE-AIM Framework.

Journal of cancer education : the official journal of the American Association for Cancer Education·2026
Same author

Scan, Screen, Support-A Digital Pathway for Assessing Supportive Care Needs in Oncology: Implementation Study.

JMIR cancer·2026
Same author

Scan, Screen, Support - A Digital Pathway to Assess Supportive Care Needs in Oncology: An Implementation Study.

JMIR cancer·2026
Same author

From knowledge to action: strengthening cancer prevention knowledge in schools among adolescents in Germany.

BMC public health·2026

Related Experiment Video

Updated: Jan 21, 2026

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)
09:05

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)

Published on: June 12, 2017

30.7K

Dynamic Survival Analysis Using In-Memory Technology.

Sophie Schneiderbauer1, Diana Schweizer1, Theres Fey1

  • 1Comprehensive Cancer Center Munich - LMU, Hospital of the Ludwig-Maximilians-University Munich, Bavaria, Germany.

Studies in Health Technology and Informatics
|July 27, 2019
PubMed
Summary

This study introduces a real-time oncology analysis platform for survival analysis. It enables instant adjustments to survival curves, improving therapy success evaluation and risk factor assessment.

Keywords:
Data WarehousingMedical OncologySurvival Analysis

More Related Videos

A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

12.0K
Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis
09:02

Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis

Published on: February 20, 2021

3.4K

Related Experiment Videos

Last Updated: Jan 21, 2026

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)
09:05

Assessing Working Memory in Children: The Comprehensive Assessment Battery for Children – Working Memory (CABC-WM)

Published on: June 12, 2017

30.7K
A Real-world What-Where-When Memory Test
09:13

A Real-world What-Where-When Memory Test

Published on: May 16, 2017

12.0K
Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis
09:02

Monitoring Influenza Virus Survival Outside the Host Using Real-Time Cell Analysis

Published on: February 20, 2021

3.4K

Area of Science:

  • Oncology
  • Biostatistics

Background:

  • Survival analysis is crucial in oncology for measuring therapy success and evaluating prognostic factors.
  • Traditional statistical methods require extensive scripting for each analytical request.

Purpose of the Study:

  • To develop a real-time analysis platform for oncology survival analysis.
  • To enable spontaneous adjustments of survival curves based on comprehensive oncological data.

Main Methods:

  • Integration of common survival analyses into a real-time platform.
  • Utilization of an in-memory database for efficient data handling.
  • Application to a comprehensive oncological dataset.

Main Results:

  • The platform allows for spontaneous adjustments to survival curves.
  • Instantaneous results can be deduced, unlike classical statistical approaches.
  • Facilitates dynamic analysis of therapy success and risk factors.

Conclusions:

  • The developed platform offers an efficient and dynamic approach to oncology survival analysis.
  • It streamlines the evaluation of therapy success and prognostic factors.
  • Enables real-time data-driven insights for oncological research and clinical practice.