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

328
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...
328
Actuarial Approach01:20

Actuarial Approach

63
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,...
63
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

121
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
121
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

152
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...
152
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

100
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
100
Hazard Ratio01:12

Hazard Ratio

91
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
91

You might also read

Related Articles

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

Sort by
Same author

Disseminated Tumor Cells (DTCs) in Patients with Cervical Cancer Reveal Mesenchymal Properties and Potential Therapeutic Targets-A New Perspective?

International journal of molecular sciences·2026
Same author

Constitutional <i>BRCA1</i> Promoter Methylation in Patients With Ovarian Cancer: Results of the Observational AGO-TR1 Study.

JCO precision oncology·2026
Same author

Digital Health Literacy, Technology Acceptance, and Competence Among Older Adults Aged ≥65 Years: Cross-Sectional Study Investigating Differences Between Women and Men.

Journal of medical Internet research·2026
Same author

High uPAR and Low miR-221 Expression Predict Poor Disease-Free Survival in Triple-Negative Breast Cancer.

Pathophysiology : the official journal of the International Society for Pathophysiology·2026
Same author

Allele-specific suppression of pathogenic bestrophin-1 transcripts by CRISPR/Cas9-mediated genome editing.

Genome medicine·2026
Same author

Variants in the proteasome regulator PSMF1 cause a phenotypic spectrum from parkinsonism to perinatal lethality.

Nature communications·2026

Related Experiment Video

Updated: Jun 6, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K

Calculating Future 10-Year Breast Cancer Risks in Risk-Adapted Surveillance: A Method Comparison and Application in

Silke Zachariae1, Anne S Quante2,3, Marion Kiechle2

  • 1Institute for Medical Informatics, Statistics, and Epidemiology (IMISE), Leipzig University, Leipzig, Germany.

Cancer Prevention Research (Philadelphia, Pa.)
|November 22, 2024
PubMed
Summary
This summary is machine-generated.

A new conditional probability (CP) approach simplifies predicting future breast cancer risk for high-risk women. This method, based on initial assessments, accurately identifies when women exceed the 5% threshold for annual screening, improving surveillance strategies.

More Related Videos

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
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

218

Related Experiment Videos

Last Updated: Jun 6, 2025

Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.1K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.0K
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

218

Area of Science:

  • Oncology
  • Genetics
  • Preventive Medicine

Background:

  • The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) uses risk-adapted surveillance for high-risk women.
  • Annual breast imaging is recommended for women with a predicted 10-year breast cancer risk of 5% or higher, using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model.
  • Women initially below the 5% risk threshold may cross it later, necessitating updated risk assessments.

Purpose of the Study:

  • To compare the 'aging pedigree' (AP) approach with a new 'conditional probability' (CP) approach for predicting future breast cancer risk.
  • To evaluate the practicality and accuracy of the CP approach for identifying women who will exceed the 5% 10-year breast cancer risk threshold over time.
  • To provide a simplified method for risk assessment in hereditary breast and ovarian cancer surveillance.

Main Methods:

  • Data from 6,661 women registered with GC-HBOC were analyzed.
  • The 'aging pedigree' (AP) approach involved repeating BOADICEA calculations annually with updated family history information.
  • The 'conditional probability' (CP) approach calculated future risks based on the initial BOADICEA assessment, offering a simplified alternative.

Main Results:

  • Initially, 74% of women aged 30-48 had a 10-year breast cancer risk below 5%.
  • Using the AP approach, 53% of these women exceeded the 5% threshold at an older age.
  • The CP approach showed that 99% of women initially below the threshold crossed it at the same or an earlier age compared to AP (88% within 1 year). The CP approach has been implemented as a web application.

Conclusions:

  • The conditional probability (CP) approach is a practical and accurate method for predicting future breast cancer risk in women.
  • CP simplifies the process of determining when women cross the 5% 10-year breast cancer risk threshold, facilitating timely surveillance.
  • This method supports the GC-HBOC guideline for risk-adapted breast cancer surveillance, enhancing preventive care for high-risk individuals.