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

402
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...
402
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

228
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...
228
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

160
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.
160
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

301
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...
301
Actuarial Approach01:20

Actuarial Approach

101
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,...
101
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

197
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,...
197

You might also read

Related Articles

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

Sort by
Same author

An Overview and Recent Developments in the Analysis of Multistate Processes.

Statistics in medicine·2026
Same author

Expected life years compared to the general population.

Biometrical journal. Biometrische Zeitschrift·2023
Same author

The Relative Preservation of the Central Retinal Layers in Leber Hereditary Optic Neuropathy.

Journal of clinical medicine·2022
Same author

Is It Possible to Predict Clonal Thrombocytosis in Triple-Negative Patients with Isolated Thrombocytosis Based Only on Clinical or Blood Findings?

Journal of clinical medicine·2021
Same author

COVID-19 in Slovenia, from a Success Story to Disaster: What Lessons Can Be Learned?

Life (Basel, Switzerland)·2021
Same author

Nutritional Status and Health-Related Quality of Life in Men with Advanced Castrate-Resistant Prostate Cancer.

Nutrition and cancer·2021
Same journal

Ensuring Quality in Preclinical Research: The Importance of Being Human.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Addressing Cluster-Level Treatment Effect Heterogeneity in Sample Size Determination for Hierarchical 2 × 2 Factorial Designs.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

A Multiple Imputation Approach to Distinguish Curative From Life-Prolonging Effects in the Presence of Missing Covariates.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Tests for Categorical Data Beyond Pearson: A Distance Covariance and Energy Distance Approach.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Nonparametric Estimation of the Patient-Weighted While-Alive Estimand.

Biometrical journal. Biometrische Zeitschrift·2026
Same journal

Two-Stage Multiple Test Procedures Controlling False Discovery Rate With Auxiliary Variable and Their Application to Set4 <math><semantics><mi>Δ</mi> <annotation>$\Delta$</annotation></semantics></math> Mutant Data.

Biometrical journal. Biometrische Zeitschrift·2026
See all related articles

Related Experiment Video

Updated: Jul 27, 2025

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

307

Evaluating cancer screening programs using survival analysis.

Bor Vratanar1, Maja Pohar Perme1

  • 1Institute for Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia.

Biometrical Journal. Biometrische Zeitschrift
|June 6, 2023
PubMed
Summary
This summary is machine-generated.

Cancer screening aims to improve survival through early detection. This study introduces a new method to accurately estimate survival benefits by accounting for biases like lead time and overdetection.

Keywords:
biasbreast cancercancer screeningcounterfactualsurvival analysis

More Related Videos

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

10.2K
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

2.1K

Related Experiment Videos

Last Updated: Jul 27, 2025

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

307
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

10.2K
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

2.1K

Area of Science:

  • Oncology
  • Biostatistics
  • Epidemiology

Background:

  • Cancer screening programs aim to improve patient survival by enabling early diagnosis and treatment.
  • Directly testing the survival benefit of screening requires comparing screen-detected cases with non-screened counterparts.
  • Naive comparisons are often biased due to factors like lead time, length bias, and overdetection.

Purpose of the Study:

  • To develop a formal notation for comparing the survival of screen-detected cancer cases versus non-screened populations.
  • To identify and decompose biases inherent in naive survival comparisons within screening programs.
  • To propose a novel nonparametric estimator for accurately assessing the true survival advantage of cancer screening.

Main Methods:

  • Development of a general notation for defining the comparison of interest in cancer screening studies.
  • Analysis and decomposition of biases including lead time bias, length time bias, and overdetection bias.
  • Introduction of a new nonparametric estimator to quantify the survival of the hypothetical screen-detected control group.

Main Results:

  • Naive comparisons between screen-detected and interval cancer cases are demonstrably biased.
  • Existing methods can estimate some components of bias, but a gap remains in estimating the control group's survival.
  • The proposed nonparametric estimator successfully fills this gap, enabling estimation of the true survival contrast.

Conclusions:

  • Accurate estimation of cancer screening benefits requires addressing lead time, length time, and overdetection biases.
  • The developed nonparametric estimator allows for a comprehensive assessment of screening program effectiveness.
  • This approach provides a more reliable method for evaluating the impact of cancer screening on patient survival.