Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Actuarial Approach

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,...

Preventive Healthcare Services

Preventive Healthcare Services

Preventive healthcare services keep people healthy via frequent check-ups, screening, and counseling. They primarily aid in disease prevention rather than treating an acute or chronic illness. Preventive treatment also keeps individuals productive and energetic, allowing them to work well into their retirement years. Examples of preventive care services include:

Prevalence and Incidence

Prevalence and Incidence

In statistical epidemiology and health sciences, two essential metrics—prevalence and incidence—are fundamental for understanding disease dynamics within a population. These measures enable public health officials, epidemiologists, and researchers to assess the burden of diseases, allocate resources effectively, and design impactful public health policies and interventions.
Prevalence indicates the proportion of individuals in a population who have a specific disease or health condition at a...

Bias in Epidemiological Studies

Bias in Epidemiological Studies

Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:

Cancer Survival Analysis

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...

Kaplan-Meier Approach

Kaplan-Meier Approach

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,...

You might also read

Related Articles

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

Sort by

Same author

Recruitment strategies and outcomes in lung cancer screening programmes: a scoping review.

European radiology·2026

Same author

Histological subtype and receptor characteristics of cancers detected or missed by digital mammography and tomosynthesis: Results from MAITA randomized trials.

European journal of cancer (Oxford, England : 1990)·2026

Same author

Italian guidelines for the use of digital breast tomosynthesis in breast cancer screening programmes: GRADE-ADOLOPMENT of the European guidelines.

La Radiologia medica·2026

Same author

Effectiveness of Lung Cancer Screening by Sex and Tumor Histology: Extended, Pooled Analysis of the ITALUNG and LUSI Trials, with Comparison to Findings in the NLST.

Cancer communications (London, England)·2026

Same author

Impact of whole breast irradiation, endocrine therapy or their combination on outcomes of early-stage, luminal A-like, low-risk, breast cancer patients aged 65 years or over: Insights from the EUSOMA database.

European journal of cancer (Oxford, England : 1990)·2026

Same author

Comments on "Is the incidence of early-onset cancers increasing in the northeast of Italy? A 30-year analysis of the regional cancer registration data by age, 1987-2019".

International journal of cancer·2026

Search research articles

Related Experiment Video

Updated: Jul 2, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

[Interval cancers as indicators of performance in screening programmes].

Stefano Ciatto¹, Carlo Naldoni, Antonio Ponti

¹stefano.ciatto@gmail.com

Epidemiologia E Prevenzione

|August 23, 2008

Summary

This summary is machine-generated.

Interval cancers (IC) are identified using Cancer Registries or hospital records. The most reliable measure of IC frequency is proportional incidence, crucial for evaluating screening program effectiveness.

More Related Videos

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

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: Jul 2, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

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

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
Radiology
Public Health

Background:

Interval cancers (IC) are crucial indicators of screening program efficacy.
Accurate identification and measurement of IC are essential for performance evaluation.
Existing methods for IC identification and frequency calculation vary in reliability.

Purpose of the Study:

To provide guidelines and reference standards for interval cancer (IC) analysis.
To establish reliable methods for IC identification and frequency measurement.
To recommend best practices for reviewing mammograms associated with IC.

Main Methods:

Interval cancer identification relies on Cancer Registries (CR) or hospital discharge records.
Proportional incidence (observed IC/expected carcinomas) is proposed as the optimal measure of IC frequency.
Mammogram review for IC should primarily utilize a blind procedure, mixing IC cases with negative controls.

Main Results:

Proportional incidence is identified as a more reliable metric for IC frequency compared to other formulas.
Comparing IC stage at diagnosis with screen-detected cancers and cancers in non-attenders is recommended.
Blind review of preceding mammograms is more representative of the clinical scenario and respects radiologist rights.

Conclusions:

Standardized methods for IC analysis are needed to improve screening program evaluation.
Proportional incidence offers a robust measure for assessing screening sensitivity.
Blind mammogram review protocols enhance the accuracy and fairness of assessing screening errors.