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

Actuarial Approach01:20

Actuarial Approach

77
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,...
77
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

133
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,...
133
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

363
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:
363
Life Tables01:22

Life Tables

93
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
93
Causality in Epidemiology01:21

Causality in Epidemiology

397
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
397
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

178
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...
178

You might also read

Related Articles

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

Sort by
Same author

Geographical divides in male premature mortality in the CEE-FSU European region: an ecological study of 2320 spatial units in 12 countries, 2003-2019.

BMJ public health·2026
Same author

Potential and challenges for sustainable progress in human longevity.

Nature communications·2026
Same author

Regional mortality disparities in Central and Eastern Europe 2000-22.

Journal of public health (Oxford, England)·2025
Same author

Spatial disparities in cause-specific mortality in Ukraine: A district-level analysis, 2006-19.

Population studies·2024
Same author

Spatial disparities in the mortality burden of the covid-19 pandemic across 569 European regions (2020-2021).

Nature communications·2024
Same author

Spatial Variation in Excess Mortality Across Europe: A Cross-Sectional Study of 561 Regions in 21 Countries.

Journal of epidemiology and global health·2024

Related Experiment Video

Updated: Jun 27, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K

Method for redistributing ill-defined causes of death.

Pavel Grigoriev1, Florian Bonnet2, Elsa Perdrix3,4

  • 1Federal Institute for Population Research.

Population Studies
|April 26, 2024
PubMed
Summary

This study introduces a regression-based method to accurately redistribute ill-defined deaths, improving global mortality data comparability. The approach enhances epidemiological monitoring and policy development by correcting data biases.

Keywords:
ill-defined causes of deathmethod of redistributionmortality

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
MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
07:17

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Published on: February 7, 2025

427

Related Experiment Videos

Last Updated: Jun 27, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.5K
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
MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
07:17

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Published on: February 7, 2025

427

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Cause-specific mortality data are vital for public health surveillance and policy.
  • Comparability of mortality data is compromised by varying proportions of ill-defined deaths.
  • Redistributing ill-defined deaths is necessary to eliminate bias in cause-specific mortality statistics.

Purpose of the Study:

  • To provide tools and documentation for implementing a regression-based method to redistribute ill-defined causes of death.
  • To refine and evaluate the performance of Sully Ledermann's redistribution method.
  • To enable unbiased estimation of national and subnational death rates.

Main Methods:

  • Utilized subnational cause-specific mortality data.
  • Applied a refined regression-based redistribution method, building on Ledermann's 1950s approach.
  • Conducted simulations to evaluate the method's performance.

Main Results:

  • Demonstrated a practical application using French subnational cause-of-death data.
  • Developed and provided R code for the redistribution calculations.
  • The refined method allows for unbiased estimation of cause-specific mortality rates.

Conclusions:

  • The regression-based redistribution of ill-defined deaths improves the accuracy and comparability of mortality data.
  • This method offers a valuable tool for epidemiological analysis and evidence-based public health policy.
  • Accessible R code facilitates the implementation of this technique in diverse settings.