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

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,...
Kaplan-Meier Approach01:24

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,...
Life Tables01:22

Life Tables

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,...
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.

You might also read

Related Articles

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

Sort by
Same author

North American perspective on the highly pathogenic avian influenza H5Nx clade 2.3.4.4b outbreak (November 2021-March 2025).

Canadian journal of microbiology·2026
Same author

Twenty years of ungulate disease surveillance by the Canadian Wildlife Health Cooperative (2003-2022).

PloS one·2026
Same author

Essential contributions of wildlife health surveillance to the United Nations Sustainable Development Goals.

One health outlook·2025
Same author

A community-of-practice-built database to support the implementation and operation of national and subnational wildlife health surveillance systems.

One health (Amsterdam, Netherlands)·2025
Same author

Highly Pathogenic Avian Influenza (HPAI) Detected in 41 At-risk Species in Canada.

Journal of wildlife diseases·2025
Same author

Sweating the small stuff: microclimatic exposure and species habitat associations inform climate vulnerability in a grassland songbird community.

Biology letters·2025

Related Experiment Video

Updated: Jun 25, 2026

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

Estimating cause-specific mortality rates using recovered carcasses.

Damien O Joly1, Dennis M Heisey, Michael D Samuel

  • 1Global Health Programs, Wildlife Conservation Society, 1008 Beverly Drive, Nanaimo, British Columbia, Canada, V9S 2S4. djoly@wcs.org

Journal of Wildlife Diseases
|February 11, 2009
PubMed
Summary
This summary is machine-generated.

Marine mammal stranding networks collect valuable data on causes of death. This study introduces a new method to calculate cause-specific mortality rates from this data, improving population health assessments.

More Related Videos

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

Related Experiment Videos

Last Updated: Jun 25, 2026

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

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

Area of Science:

  • Marine Biology
  • Wildlife Health
  • Conservation Science

Background:

  • Stranding networks provide long-term datasets on marine mammal and bird mortality.
  • These data are crucial for identifying mortality sources and informing management actions.
  • Previous analyses have not fully explored the utility of these datasets for determining cause-specific mortality rates.

Purpose of the Study:

  • To develop and demonstrate a statistical approach for estimating cause-specific mortality rates from stranding network data.
  • To partition total mortality rates into individual causes using a maximum likelihood framework.
  • To provide a method for deriving variance estimates for these cause-specific rates.

Main Methods:

  • A maximum likelihood-based statistical model was employed.
  • The model partitions total mortality rate, estimated independently, into cause-specific rates.
  • The methodology was applied to mortality data from California sea otters and Florida manatees.

Main Results:

  • The study successfully demonstrates a method to estimate cause-specific mortality rates from stranding data.
  • Variance estimates for these rates can be reliably derived using the proposed approach.
  • The method provides a quantitative tool for analyzing complex mortality patterns in marine populations.

Conclusions:

  • The developed maximum likelihood approach enhances the utility of stranding network data for ecological and conservation studies.
  • This method allows for more precise identification of threats to marine mammal and bird populations.
  • Accurate cause-specific mortality rates are essential for effective wildlife population management and conservation strategies.