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

Introduction To Survival Analysis01:18

Introduction To Survival Analysis

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 until a...
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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

Comparing the Survival Analysis of Two or More Groups

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 Cox...
Actuarial Approach01:20

Actuarial Approach

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,...
Hazard Rate01:11

Hazard Rate

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...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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.

You might also read

Related Articles

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

Sort by
Same author

Health inequality aversion in China: Public and decision-maker views.

Social science & medicine (1982)·2026
Same author

Comparative social costs of six early years disadvantages: a birth cohort microsimulation study.

Journal of epidemiology and community health·2026
Same author

Socioeconomic inequalities in causes of death related to behavioural risk-taking in England and Wales: A longitudinal small-area ecological study.

Public health·2026
Same author

Accounting for unmet need in equitable healthcare resource allocation: Synopsis.

Health and social care delivery research·2026
Same author

Author Reply.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research·2026
Same author

The Inequality-Adjusted Incremental Cost-Effectiveness Ratio.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research·2026
Same journal

Lead in Drinking Water and Child Health: Evidence From Jackson, Mississippi.

Health economics·2026
Same journal

Health on the Move: The Impact of Poverty Alleviation Relocation on Healthcare Utilization in China.

Health economics·2026
Same journal

The Effects of Compulsory Licensing: A Case Study of HIV Drugs.

Health economics·2026
Same journal

Beyond Tobacco Prevention: The Effects of Tobacco 21 Laws on Young Adults' Body Weight.

Health economics·2026
Same journal

Assessing the Estimands and Estimates of Hospitalization Rates in Health Economics and Clinical Medicine.

Health economics·2026
Same journal

The Impact of Unemployment Insurance Benefit Cuts on Mental Health: Evidence From Early Pandemic Program Expirations.

Health economics·2026
See all related articles

Related Experiment Video

Updated: Jun 23, 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

Analysing risk attitudes to time.

Adam Oliver1, Richard Cookson

  • 1LSE Health and Social Care, London School of Economics and Political Science, London, UK. a.j.oliver@lse.ac.uk

Health Economics
|May 9, 2009
PubMed
Summary
This summary is machine-generated.

The standard Quality-Adjusted Life Year (QALY) model assumes risk neutrality, but this study found significant risk aversion over life years. Alternative methods did not reduce this observed risk aversion in participants.

More Related Videos

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Related Experiment Videos

Last Updated: Jun 23, 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

Measuring Delay Discounting in Humans Using an Adjusting Amount Task
07:47

Measuring Delay Discounting in Humans Using an Adjusting Amount Task

Published on: January 9, 2016

Area of Science:

  • Health Economics
  • Decision Analysis
  • Behavioral Economics

Background:

  • The Quality-Adjusted Life Year (QALY) model, widely used in health technology assessment by bodies like NICE, assumes individuals are risk-neutral regarding discounted life years.
  • This assumption of risk neutrality is fundamental to cost-utility analyses (CUA) worldwide.

Purpose of the Study:

  • To empirically test the assumption of risk neutrality over discounted life years in a sample population.
  • To investigate if the lottery equivalents method can mitigate observed risk aversion compared to the standard gamble.

Main Methods:

  • A probability equivalence version of the standard gamble was administered to 30 respondents to assess risk preferences over life years.
  • A separate sample of 40 respondents was assessed using the lottery equivalents method to evaluate its effect on risk aversion.

Main Results:

  • The study revealed significant risk aversion over discounted life years, contradicting the standard assumption of risk neutrality.
  • The lottery equivalents method did not reduce the observed level of risk aversion; in fact, it was comparable to or higher than that seen with the standard gamble.

Conclusions:

  • The findings challenge the universal applicability of the risk-neutral assumption in QALY-based cost-utility analyses.
  • Further research is necessary to determine the generalizability of risk aversion over discounted life years and its implications for health policy.