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

Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

167
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.
167
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

512
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
512
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

320
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...
320
Survival Curves01:18

Survival Curves

235
Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
235
Actuarial Approach01:20

Actuarial Approach

106
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,...
106
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

212
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,...
212

You might also read

Related Articles

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

Sort by
Same author

Framework for evaluating in vitro vasculogenic mimicry using structural and metabolic parameters.

Cell reports methods·2026
Same author

First report and genomic characterization of an mcr-10.1-carrying Enterobacter kobei strain isolated from a domestic kitchen sink in China.

BMC microbiology·2026
Same author

Correction: A transmission relationship investigation of HIV infection through male-to-male sex among a case of left-behind children with heterosexual orientation in Zhejiang Province of China.

Frontiers in public health·2026
Same author

Integrated Phenotypic and Genomic Analysis of Antimicrobial Resistance, Virulence, and Phylogeny in <i>Vibrio cholerae</i> Isolates from Jiaxing, China, with Emphasis on Non-O1/Non-O139 Strains.

Microorganisms·2026
Same author

Genomic characterization of clinical and environmental <i>Vibrio cholerae</i> O1 and O139 isolates in Jiaxing, China, with identification of a <i>ctx</i>-positive O139 strain harboring an IncC plasmid.

Frontiers in microbiology·2026
Same author

Genetic and Phenotypic Characterization of a <i>Salmonella Enteritidis</i> ST11 Clinical Isolate Carrying <i>bla</i><sub>NDM-13</sub> in Jiaxing City, China.

Antibiotics (Basel, Switzerland)·2026

Related Experiment Video

Updated: Aug 7, 2025

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

34.5K

Managing Mortality and Aging Risks with a Time-Varying Lee-Carter Model.

Zhongwen Chen1, Yanlin Shi2, Ao Shu3

  • 1College of Economics and Management, Hengyang Normal University, Hengyang 421002, China.

Healthcare (Basel, Switzerland)
|March 11, 2023
PubMed
Summary

This study introduces an improved Lee-Carter model for mortality forecasting. The enhanced model accurately captures changing mortality decline rates, improving long-term forecast reliability for multiple countries.

Keywords:
Lee–Carter modellife expectancymortality ratesrotated age patterntime-varying coefficients

More Related Videos

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
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

10.3K

Related Experiment Videos

Last Updated: Aug 7, 2025

Measurement of Lifespan in Drosophila melanogaster
10:00

Measurement of Lifespan in Drosophila melanogaster

Published on: January 7, 2013

34.5K
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
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

10.3K

Area of Science:

  • Demography
  • Actuarial Science
  • Biostatistics

Background:

  • Existing research indicates mortality declines are not static, decelerating at young ages and accelerating at old ages.
  • The standard Lee-Carter (LC) model's long-term mortality forecasts are less reliable due to its inability to account for these dynamic patterns.

Purpose of the Study:

  • To introduce a time-varying coefficients extension of the Lee-Carter model for more accurate mortality forecasting.
  • To incorporate age-specific patterns of mortality decline into forecasting models.

Main Methods:

  • Developed a time-varying coefficients extension of the Lee-Carter model using effective kernel methods.
  • Implemented two kernel functions: Epanechnikov (LC-E) and Gaussian (LC-G).
  • Extended the model to handle multi-population scenarios.

Main Results:

  • The proposed LC-E and LC-G models consistently improve forecasting accuracy compared to the standard LC and Li-Lee models.
  • The enhanced models effectively incorporate the observed rotating patterns of mortality decline.
  • The models demonstrate improved accuracy in both single- and multi-population forecasting scenarios.

Conclusions:

  • The time-varying coefficients extension of the Lee-Carter model provides more reliable long-term mortality forecasts.
  • The proposed kernel-based methods are practical, adaptable, and enhance forecasting precision.
  • This approach offers a significant advancement for demographic and actuarial mortality projections.