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

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

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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 until a...
Survival Curves01:18

Survival Curves

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...
Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...

You might also read

Related Articles

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

Sort by
Same author

International disruptions to cancer diagnosis and stage at presentation during the COVID-19 pandemic in 2020: an International Cancer Benchmarking Partnership (ICBP) population-based study.

The Lancet. Oncology·2026
Same author

Development of a population based patient cancer data warehouse from multiple electronic health record systems.

Health informatics journal·2026
Same author

Projected estimates of cancer in Canada in 2026.

CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne·2026
Same author

CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne·2026
Same author

Canadian Prostate Cancer Trends in the Context of PSA Screening Guideline Changes.

Current oncology (Toronto, Ont.)·2025
Same author

Chemotherapy use in ovarian cancer patients diagnosed 2012-2017 in Australia, Canada, Norway and the UK: An International Cancer Benchmarking Partnership (ICBP) population-based study.

Cancer epidemiology·2025

Related Experiment Video

Updated: May 30, 2026

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

Conditional survival analyses across cancer sites.

Larry F Ellison1, Heather Bryant, Gina Lockwood

  • 1Health Statistics Division at Statistics Canada, Ottawa, Ontario K1A 0T6. larry.ellison@statcan.gc.ca

Health Reports
|August 19, 2011
PubMed
Summary

Cancer survival estimates improve significantly after five years for many cancers. High conditional survival rates are achievable even for cancers with initially poor prognoses, offering hope for improved long-term outcomes.

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Related Experiment Videos

Last Updated: May 30, 2026

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

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Area of Science:

  • Oncology
  • Biostatistics
  • Cancer Epidemiology

Background:

  • Traditional survival estimates lose relevance beyond the initial years post-diagnosis.
  • Conditional survival ratios (CSR) offer a more dynamic measure of long-term cancer prognosis.
  • Assessing CSR provides insights into patient outcomes beyond the first one or two years.

Purpose of the Study:

  • To evaluate the long-term survival prospects of various cancers using conditional relative survival ratios (RSR).
  • To determine if survival rates improve significantly after the initial five years for different cancer types.
  • To identify cancers that show substantial improvement in conditional survival over time.

Main Methods:

  • Utilized Canadian Cancer Registry and Canadian Vital Statistics Death Database records.
  • Calculated five-year conditional relative survival ratio (RSR) estimates for numerous cancer types.
  • Analyzed RSR changes at the five-year post-diagnosis mark.

Main Results:

  • Cancers with initial five-year RSR ≥ 80% (excluding breast cancer) achieved ≥ 95% conditional five-year RSR after five years.
  • Cervix uteri and colon cancers (initial RSR 50-79%) reached ≥ 95% conditional five-year RSR after five years.
  • Stomach cancer and leukemia (excluding CLL) showed ≥ 90% conditional five-year RSR after five years, despite initial poor prognoses.

Conclusions:

  • Conditional survival analysis reveals significant long-term survival improvements for many cancers.
  • Five-year survival is a critical juncture for assessing prognosis shifts in various malignancies.
  • Even cancers with initially low survival rates can demonstrate substantial long-term survival gains.