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

Cancer Survival Analysis01:21

Cancer Survival Analysis

665
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...
665
Survival Tree01:19

Survival Tree

418
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...
418
Survival Curves01:18

Survival Curves

696
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...
696
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

765
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...
765
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

583
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...
583
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

611
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...
611

You might also read

Related Articles

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

Sort by
Same author

Driver vs. passenger: Primary resistance to larotrectinib in synovial sarcoma harboring concurrent SS18 rearrangement and PDE3A-NTRK2 fusion.

Tumori·2026
Same author

Prognostic Value of Liver Function-based Scores in Hepatocellular Carcinoma Patients Undergoing Liver Transplantation.

In vivo (Athens, Greece)·2026
Same author

Adjuvant Chemotherapy and Survival After Pelvic Exenteration for Gynecologic Cancers in the COREPEX Study.

JAMA network open·2026
Same author

Gender Disparities in Response to Neoadjuvant Therapy for Early-Stage Breast Cancer: A Propensity Score-Matched Analysis of the SEER Database.

Breast care (Basel, Switzerland)·2026
Same author

Assessment of Large Language Models in Colorectal Cancer Multidisciplinary Tumor Board Decision-Making: A Retrospective Single-Center Comparison of Guideline-Integrated General-Purpose vs. Domain-Specialized Models.

Current oncology (Toronto, Ont.)·2026
Same author

In Regard to Karp et al.

International journal of radiation oncology, biology, physics·2026

Related Experiment Video

Updated: Jan 28, 2026

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

Shifting Survival Horizons in Advanced Ovarian Cancer: A Conditional Survival Perspective.

Aydan Farzaliyeva1, Huseyin Akilli2, Ozden Altundag1

  • 1Department of Medical Oncology, Baskent University Faculty of Medicine, 06490 Ankara, Türkiye.

Current Oncology (Toronto, Ont.)
|January 27, 2026
PubMed
Summary

Conditional survival analysis shows that prognosis for advanced ovarian cancer improves over time. This offers a dynamic view of long-term outcomes for patients with epithelial ovarian cancer.

Keywords:
conditional survivaldisease progressionlong-term survivalovarian carcinomaprognosis

More Related Videos

Survivable Stereotaxic Surgery in Rodents
00:08

Survivable Stereotaxic Surgery in Rodents

Published on: October 6, 2008

70.1K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

663

Related Experiment Videos

Last Updated: Jan 28, 2026

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
Survivable Stereotaxic Surgery in Rodents
00:08

Survivable Stereotaxic Surgery in Rodents

Published on: October 6, 2008

70.1K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

663

Area of Science:

  • Oncology
  • Clinical Epidemiology

Background:

  • Advanced-stage epithelial ovarian cancer (EOC) presents significant heterogeneity and poor prognoses.
  • Traditional survival metrics do not capture the evolving nature of EOC prognosis as patients live longer.

Purpose of the Study:

  • To evaluate conditional survival (CS) in advanced EOC using overall survival (OS) and progression-free survival (PFS) metrics.
  • To provide a dynamic understanding of long-term outcomes for advanced EOC patients.

Main Methods:

  • Retrospective analysis of 808 patients with FIGO stage III-IV EOC treated between 2004 and 2024.
  • Calculation of CS estimates for additional 1- and 5-year intervals based on prior survival milestones (6 months, 1, 3, 5 years).

Main Results:

  • Median overall survival (OS) was 4.37 years; median progression-free survival (PFS) was 1.70 years.
  • Peritoneal dissemination and platinum resistance were independent predictors of poor survival.
  • Conditional survival estimates demonstrated a significant increase in both CS-OS and CS-PFS over time, indicating improving prognosis with sustained survival and disease control.

Conclusions:

  • Prognosis in advanced ovarian cancer is dynamic and improves with time and sustained disease control.
  • Conditional survival analysis redefines long-term outcomes, offering a modern approach for patient counseling and survivorship planning.