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

176
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.
176
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

556
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...
556
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

355
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...
355
Cancer Survival Analysis01:21

Cancer Survival Analysis

429
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...
429
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

235
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,...
235
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

261
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...
261

You might also read

Related Articles

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

Sort by
Same author

Reliability, bias, and computational cost of estimating the Bayes factor using bridge sampling and the Savage-Dickey density ratio.

Behavior research methods·2026
Same author

Are Semantic Representations Stable? A Bayesian Framework Applied to the Study of Quantifier Meaning.

Computational brain & behavior·2026
Same author

No evidence that nonincentivized behavioral interventions effectively mitigate climate change after adjusting for publication bias.

PNAS nexus·2026
Same author

Robust Bayesian multilevel meta-analysis: Adjusting for publication bias in the presence of dependent effect sizes.

Behavior research methods·2026
Same author

Comparing variable selection and model averaging methods for logistic regression.

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

Investigating the reproducibility of the social and behavioural sciences.

Nature·2026
Same journal

Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.

BMC medical research methodology·2026
Same journal

Sparse multi-way DMDC for longitudinal classification in high dimension low sample size data.

BMC medical research methodology·2026
Same journal

Tree-based exploratory identification of predictive biomarkers in non-randomized data.

BMC medical research methodology·2026
Same journal

Comparative evaluation of interrupted time series analytical methods for healthcare quality improvement research: a Monte Carlo simulation study.

BMC medical research methodology·2026
Same journal

Methodological advances in claims-based dementia algorithms: integrating medication and clinical data for medicare populations.

BMC medical research methodology·2026
Same journal

An interpretable XGboost algorithm for predicting 30-day mortality in acute pancreatitis using routine biomarkers.

BMC medical research methodology·2026
See all related articles

Related Experiment Video

Updated: Aug 29, 2025

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.5K

Informed Bayesian survival analysis.

František Bartoš1,2, Frederik Aust3, Julia M Haaf3

  • 1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands. f.bartos96@gmail.com.

BMC Medical Research Methodology
|September 10, 2022
PubMed
Summary
This summary is machine-generated.

Bayesian parametric survival analysis offers advantages over frequentist methods, including faster clinical trial completion and more efficient data use. This approach aids in estimating treatment effects and provides richer inferences for survival data.

Keywords:
Bayes factorBayesianHistorical dataModel-averagingSurvival 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

10.3K
Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.6K

Related Experiment Videos

Last Updated: Aug 29, 2025

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions
05:18

Measurement of Survival Time in Brachionus Rotifers: Synchronization of Maternal Conditions

Published on: July 22, 2016

8.5K
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
Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy
07:02

Monitoring Neuronal Survival via Longitudinal Fluorescence Microscopy

Published on: January 19, 2019

6.6K

Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Statistical Modeling

Background:

  • The dominant frequentist approach in survival analysis has limitations.
  • Bayesian methods offer advantages like incorporating historical data and handling uncertainty.
  • Parametric survival analysis benefits from Bayesian estimation, hypothesis testing, and model-averaging.

Purpose of the Study:

  • To provide an overview of Bayesian parametric survival analysis.
  • To contrast Bayesian and frequentist approaches in survival analysis.
  • To illustrate the benefits of Bayesian methods using a colon cancer trial example.

Main Methods:

  • Retrospective re-analysis of a colon cancer trial using Bayesian parametric survival analysis.
  • Simulation study comparing Bayesian and frequentist models in fixed-n and sequential designs.
  • Assessment of performance metrics including decision time, power, error rates, bias, and RMSE.

Main Results:

  • Bayesian analysis indicated no positive treatment effect for Cetuximab in colon cancer patients.
  • Bayesian sequential analysis could have terminated the trial 10.3 months earlier.
  • Bayesian methods reached decisions faster in sequential designs, maintained power, and had appropriate false-positive rates.
  • Under model misspecification, Bayesian methods showed higher false-negative rates; in fixed-n designs, they had slightly higher power and lower bias/RMSE in small samples.

Conclusions:

  • Bayesian parametric survival analysis offers benefits such as more efficient data utilization.
  • This framework can considerably shorten clinical trial durations.
  • It provides a richer set of inferences compared to traditional methods.