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

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric Survival Analysis: Weibull and Exponential Methods

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...

Survival Curves

Survival Curves

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...

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,...

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,...

Applications of Life Tables

Applications of Life Tables

Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...

Life Tables

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,...

You might also read

Related Articles

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

Sort by

Same author

Ensemble forecasts of COVID-19 activity to support Australia's pandemic response: 2020-22.

PLoS computational biology·2026

Same author

Historical trends in breast cancer presentation among women in Pakistan from join-point regression analysis.

Pakistan journal of medical sciences·2024

Same author

Forecasting Total Fertility Rates For Urban And Rural Areas In Pakistan With A Coherent Functional Model.

JPMA. The Journal of the Pakistan Medical Association·2023

Same author

Understanding links between water-quality variables and nitrate concentration in freshwater streams using high frequency sensor data.

PloS one·2023

Same author

Forecasting COVID-19 activity in Australia to support pandemic response: May to October 2020.

Scientific reports·2023

Same author

Probabilistic Forecasts Using Expert Judgment: The Road to Recovery From COVID-19.

Journal of travel research·2023

Search research articles

Related Experiment Video

Updated: May 17, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Coherent mortality forecasting: the product-ratio method with functional time series models.

Rob J Hyndman¹, Heather Booth, Farah Yasmeen

¹Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800, Australia. rob.hyndman@monash.edu

|October 12, 2012

Summary

Forecasting subpopulation mortality rates can be challenging. This study introduces a coherent method using functional principal components models, improving forecast accuracy and consistency across groups.

Related Experiment Videos

Last Updated: May 17, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Area of Science:

Demography
Biostatistics
Epidemiology

Background:

Independent mortality forecasts for subpopulations often diverge long-term.
Accurate, coherent forecasting is crucial for demographic and health planning.

Purpose of the Study:

To propose a novel method for coherent mortality rate forecasting across multiple subpopulations.
To improve the accuracy and consistency of long-term mortality predictions.

Main Methods:

Utilized functional principal components models for interpretable functions of mortality rates.
Developed a product-ratio functional forecasting approach.
Imposed coherence by constraining forecast ratio functions using stationary time series models.

Main Results:

Applied the method to sex-specific (Sweden) and state-specific (Australia) mortality data.
Out-of-sample forecasts demonstrated comparable or superior accuracy to independent methods.
Achieved homogenized forecast accuracy across different subpopulations.

Conclusions:

The proposed product-ratio functional forecasting method provides coherent and accurate predictions for subpopulation mortality rates.
This approach offers a significant improvement over traditional independent forecasting models.
Ensures more reliable demographic and public health projections.