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

Kaplan-Meier Approach01:24

Kaplan-Meier Approach

788
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,...
788
Cancer Survival Analysis01:21

Cancer Survival Analysis

860
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...
860
Actuarial Approach01:20

Actuarial Approach

384
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,...
384
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

627
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,...
627
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

1.3K
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
1.3K
Probability Laws01:49

Probability Laws

29.7K
Overview
29.7K

You might also read

Related Articles

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

Sort by
Same author

Complete cancer prevalence in Europe in 2020 by disease duration and country (EUROCARE-6): a population-based study.

The Lancet. Oncology·2024
Same author

Toward Self-Powered Sensing and Thermal Energy Harvesting in High-Performance Composites v<i>ia</i> Self-Folded Carbon Nanotube Honeycomb Structures.

ACS applied materials & interfaces·2023
Same author

Estimating complete cancer prevalence in Europe: validity of alternative vs standard completeness indexes.

Frontiers in oncology·2023
Same author

Bayesian hierarchical models and prior elicitation for fitting psychometric functions.

Frontiers in computational neuroscience·2023
Same author

Key performance indicators of breast cancer screening programmes in Italy, 2011-2019.

Annali dell'Istituto superiore di sanita·2022
Same author

Single CT colonography versus three rounds of faecal immunochemical test for population-based screening of colorectal cancer (SAVE): a randomised controlled trial.

The lancet. Gastroenterology & hepatology·2022

Related Experiment Video

Updated: Apr 27, 2026

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

2.3K

Estimating cancer incidence using a Bayesian back-calculation approach.

Leonardo Ventura1, Maura Mezzetti

  • 1Cancer Prevention and Research Institute, Florence, Italy.

Statistics in Medicine
|June 25, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian model to calculate cancer incidence from mortality data, incorporating survival uncertainty and age-period-cohort projections for improved accuracy. The model reconstructs incident cases using a novel approach validated on stomach cancer data.

Keywords:
Bayesian hierarchical modelcure modelincidencemortalitysurvival

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

9.9K
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

1.0K

Related Experiment Videos

Last Updated: Apr 27, 2026

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

2.3K
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

9.9K
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

1.0K

Area of Science:

  • Biostatistics
  • Epidemiology
  • Cancer Research

Background:

  • Estimating cancer incidence from mortality data is challenging due to complex survival dynamics.
  • Existing models often do not fully account for the uncertainty inherent in survival distributions.

Purpose of the Study:

  • To develop a Bayesian hierarchical model for calculating cancer incidence counts from mortality data.
  • To incorporate survival distribution uncertainty using a Bayesian-mixture cure model.
  • To generate cancer incidence projections using a Bayesian age-period-cohort model.

Main Methods:

  • A Bayesian hierarchical model utilizing a convolution equation linking mortality, incidence, and survival probability.
  • Integration of a Bayesian-mixture cure model to handle survival uncertainty.
  • Application of a Bayesian age-period-cohort model for projections.
  • Estimation via the Gibbs sampler algorithm.
  • Validation using stomach cancer data from the Tuscany Cancer Registry.

Main Results:

  • The model successfully reconstructs incident cases by accounting for survival distribution uncertainty.
  • The integrated approach within a directed acyclic graph (DAG) model provides a robust framework.
  • Flexible survival distributions allow analysis of various cancer types and trends.

Conclusions:

  • The proposed Bayesian model offers a novel and comprehensive approach to estimating cancer incidence from mortality data.
  • The model's ability to incorporate survival uncertainty and provide projections enhances epidemiological analysis.
  • The flexible framework is adaptable for diverse cancer research applications.