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

834
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...
834
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

261
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.
261
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

532
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...
532
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

415
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...
415
Actuarial Approach01:20

Actuarial Approach

200
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,...
200
Cancer Survival Analysis01:21

Cancer Survival Analysis

533
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...
533

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 27, 2025

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

10.6K

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

Daniel Gallacher1, Peter Kimani1, Nigel Stallard1

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

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|December 7, 2020
PubMed
Summary

Selecting statistical models for health technology assessment requires more than goodness-of-fit. Overreliance on criteria like Akaike

Keywords:
Monte Carlo simulationcancerextrapolationhealth technology assessmentsurvival analysis

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

2.4K
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 27, 2025

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

10.6K
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

2.4K
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:

  • Extrapolations of parametric survival models are crucial for health technology assessment, especially for diseases with reduced life expectancy.
  • Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) are commonly used for model selection in health technology assessment.
  • Model selection for long-term treatment effect prediction requires careful consideration beyond simple goodness-of-fit statistics.

Purpose of the Study:

  • To compare the fit and restricted mean survival time (RMST) estimates from eight parametric models.
  • To contrast models selected by AIC, BIC, and log-likelihood without considering plausibility.
  • To assess the suitability of different model selection methods through simulations based on clinical trial data.

Main Methods:

  • Simulated data replicating time-to-event outcomes from four clinical trials, considering recruitment duration and follow-up times.
  • Performed 10,000 simulations for each scenario to evaluate model selection methods.
  • Compared model fit and RMST estimates from eight parametric survival models.

Main Results:

  • Different model selection methods can lead to disagreements on the best-fitting model.
  • Solely relying on goodness-of-fit statistics without considering hazard behavior and extrapolation plausibility is inappropriate.
  • Typical clinical trial follow-up durations are often insufficient for reliable extrapolation, particularly for multi-parameter models.

Conclusions:

  • Selecting survival models based only on goodness-of-fit statistics is unsuitable for cost-effectiveness analysis due to high uncertainty.
  • Bayesian Information Criterion (BIC) demonstrates superiority over other methods when follow-up data is more mature, yielding more accurate and less biased RMST estimates.
  • Model selection for survival data extrapolation necessitates a comprehensive approach, integrating goodness-of-fit with clinical plausibility and hazard behavior assessment.