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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

826
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...
826
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

402
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...
402
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

509
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...
509
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

251
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.
251
Actuarial Approach01:20

Actuarial Approach

190
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,...
190
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

382
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,...
382

You might also read

Related Articles

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

Sort by
Same author

Discovery of Novel Magnetocardiography Parameters Predicting Future ICD Therapies: Insights From the Magneto-SCD Study.

Circulation. Arrhythmia and electrophysiology·2026
Same author

Translating potential improvement in the precision and accuracy of lung nodule measurements on computed tomography scans by software derived from artificial intelligence into impact on clinical practice-a simulation study.

BJR artificial intelligence·2026
Same author

Not All Randomized Control Trials Are the Same: Response.

The American journal of sports medicine·2026
Same author

Group-Sequential Designs With an Externally-Driven Change of Primary Endpoint.

Statistics in medicine·2025
Same author

In Silico Clinical Trials in Drug Development: A Systematic Review.

Therapeutic innovation & regulatory science·2025
Same author

Variation within and between digital pathology and light microscopy for the diagnosis of histopathology slides: blinded crossover comparison study.

Health technology assessment (Winchester, England)·2025

Related Experiment Video

Updated: Nov 16, 2025

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

2.3K

Extrapolating Parametric Survival Models in Health Technology Assessment Using Model Averaging: A Simulation Study.

Daniel Gallacher1, Peter Kimani1, Nigel Stallard1

  • 1University of Warwick, Warwick Medical School, Coventry, UK.

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|February 25, 2021
PubMed
Summary

Model averaging improves restricted mean survival time (RMST) estimates in health technology appraisal by assessing model plausibility. Averaging methods are superior to single model selection, especially with complex data, enhancing reliability for decision-making.

Keywords:
Monte Carlo simulationcancerextrapolationhealth technology assessmentsurvival analysis

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

10.5K
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

14.9K

Related Experiment Videos

Last Updated: Nov 16, 2025

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

2.3K
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

10.5K
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

14.9K

Area of Science:

  • Health economics
  • Biostatistics
  • Survival analysis

Background:

  • Routine model selection methods may lack reliability for survival data extrapolation in health technology appraisal.
  • Restricted mean survival time (RMST) estimates are crucial for decision-making but can be sensitive to extrapolation uncertainty.

Purpose of the Study:

  • To enhance the reliability of RMST estimates by assessing model plausibility and implementing model averaging techniques.
  • To compare the performance of model averaging against traditional single model selection criteria (AIC, BIC) for survival data extrapolation.

Main Methods:

  • Applied plausibility assessment to remove inadequate extrapolation models.
  • Implemented model averaging using equal weighting within AIC/BIC thresholds and BIC-weighted averages.
  • Compared methods using simulation data from 12 trial scenarios (10,000 runs).

Main Results:

  • Removing implausible models reduced mean squared error and improved accuracy of RMST estimates.
  • Model averaging generally outperformed single model selection, particularly with complex hazard rates.
  • Averaging with wide criterion-based thresholds was superior to BIC-weighted averaging in most scenarios.

Conclusions:

  • Model averaging enhances the reliability of survival data extrapolation in health technology appraisal.
  • Funders should consider the uncertainty in extrapolations, especially with complex data, and encourage extended trial follow-up.