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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

580
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
580
Actuarial Approach01:20

Actuarial Approach

385
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,...
385
Hazard Rate01:11

Hazard Rate

522
The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
522
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

996
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...
996
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

498
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.
498

You might also read

Related Articles

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

Sort by
Same author

Cost-utility analysis of the Circle of Security-Parenting programme to reduce perinatal psychopathology in birthing parents in England.

BMJ open·2026
Same author

Acceptability of Circle of Security-Parenting groups in NHS community perinatal mental health services in England: parent and practitioner perspectives.

BMC psychology·2026
Same author

Discrete Choice Experiment (DCE) as a Tool to Elicit Patient Preferences in a Complex Benefit-Risk Evaluation: A Case Study.

Clinical drug investigation·2026
Same author

Pandemic risk-related behaviour change in England from June 2020 to March 2022: the cross-sectional REACT-1 study among over 2 million people.

BMJ public health·2025
Same author

Using inverse probability of censoring weighting to estimate hypothetical estimands in clinical trials: Should we implement stabilisation, and if so how?

Statistical methods in medical research·2025
Same author

Clinical effectiveness of the Circle of Security-Parenting group intervention for birthing parents in perinatal mental health services in England (COSI): a pragmatic, multicentre, assessor-masked, randomised controlled trial.

The lancet. Psychiatry·2025

Related Experiment Video

Updated: Apr 18, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.5K

A Bayesian approach to probabilistic sensitivity analysis in structured benefit-risk assessment.

Ed Waddingham1, Shahrul Mt-Isa1, Richard Nixon2

  • 1Imperial Clinical Trials Unit, School of Public Health, Imperial College London, St. Mary's Campus, Norfolk Place, London W2 1PG, UK.

Biometrical Journal. Biometrische Zeitschrift
|January 30, 2015
PubMed
Summary

This study introduces a Bayesian method to quantify uncertainty in benefit-risk assessments using multiple criteria decision analysis (MCDA). It enables robust comparison of treatments by modeling trial data and simulating benefit-risk balance variability.

Keywords:
BayesBenefit riskDecision makingMCDAStatistics

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

2.8K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K

Related Experiment Videos

Last Updated: Apr 18, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

12.5K
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

2.8K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K

Area of Science:

  • Health economics
  • Biostatistics
  • Decision science

Background:

  • Multiple criteria decision analysis (MCDA) aids benefit-risk assessment by formalizing trade-offs.
  • Current methods lack robust uncertainty propagation for treatment effects in benefit-risk balance.

Purpose of the Study:

  • To present a novel Bayesian statistical method for benefit-risk assessment.
  • To enable uncertainty propagation of treatment effects within MCDA models.

Main Methods:

  • Developed a Bayesian approach to model outcomes from randomized placebo-controlled trials.
  • Inferred indirect treatment comparisons between active treatments.
  • Utilized Markov Chain Monte Carlo simulation to derive the benefit-risk balance distribution.

Main Results:

  • Treatment effect estimates are suitable for MCDA.
  • The method successfully derives the distribution of the overall benefit-risk balance.
  • Demonstrated utility with a case study of natalizumab for multiple sclerosis.

Conclusions:

  • The proposed Bayesian method enhances transparency and rigor in benefit-risk assessment.
  • It provides a framework for quantifying the variability of the benefit-risk balance.
  • Facilitates informed decision-making in comparative treatment evaluations.