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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

692
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...
692
Cancer Survival Analysis01:21

Cancer Survival Analysis

812
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...
812
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

475
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.
475
Actuarial Approach01:20

Actuarial Approach

356
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,...
356
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

692
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,...
692
Longitudinal Research02:20

Longitudinal Research

13.6K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
13.6K

You might also read

Related Articles

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

Sort by
Same author

Associations of county-level school expenditure around time of birth and cardiovascular health in young adulthood.

American journal of preventive cardiology·2026
Same author

Authors Respond to "Racial and ethnic variation in socioeconomic differentials in young adult cardiovascular health".

American journal of epidemiology·2026
Same author

Leisure Activities are Associated with Physical and Mental Health.

Current research in behavioral sciences·2026
Same author

Adverse Pregnancy Outcomes and Cardiovascular Health Among Offspring in Early Adulthood.

JAMA network open·2026
Same author

Depression and cognition: is there a bidirectional relationship?

Aging & mental health·2026
Same author

The Latino health experience: Past and future.

Proceedings of the National Academy of Sciences of the United States of America·2026

Related Experiment Video

Updated: Mar 17, 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

11.0K

What Matters Most for Predicting Survival? A Multinational Population-Based Cohort Study.

Noreen Goldman1, Dana A Glei2, Maxine Weinstein2

  • 1Office of Population Research, Princeton University, Princeton, NJ, United States of America.

Plos One
|July 20, 2016
PubMed
Summary

Limitations in daily activities, mobility, and self-assessed health are strong predictors of mortality in older adults across four countries. These findings aid in developing better survival prediction tools.

More Related Videos

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

995

Related Experiment Videos

Last Updated: Mar 17, 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

11.0K
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

995

Area of Science:

  • Gerontology
  • Epidemiology
  • Biostatistics

Background:

  • Identifying robust predictors of mortality is crucial for public health and clinical practice.
  • Previous research has explored numerous factors but lacked comprehensive, cross-national comparisons of predictor strength.

Purpose of the Study:

  • To statistically rank the relative strength of a comprehensive set of predictors for five-year mortality in older adults.
  • To identify consistent predictors of survival across diverse national contexts.

Main Methods:

  • Analysis of four large, national, prospective cohort studies from England, the US, Costa Rica, and Taiwan.
  • Evaluation of 57 demographic, social, health, and biological variables, with 25 common across all samples.
  • Statistical ranking using the area under the receiver operating characteristic curve (AUC) and additional discrimination measures, controlling for age and sex.

Main Results:

  • Self-reported limitations in instrumental activities of daily living, mobility, and overall health were top predictors in all four countries.
  • Biomarkers (C-reactive protein, inflammatory markers, homocysteine, albumin) and performance assessments (gait speed, grip strength, chair stands) also showed strong predictive power.
  • Consistent findings across countries suggest universal applicability of these predictors.

Conclusions:

  • Functional limitations and self-assessed health are key, universally applicable predictors of mortality in older populations.
  • Incorporating these and biological markers can significantly improve mortality prediction models for both clinical and population health.
  • Enhanced prognostic tools can offer deeper insights into health disparities and inform end-of-life care discussions.