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

Bioequivalence Data: Statistical Interpretation01:16

Bioequivalence Data: Statistical Interpretation

194
Body:The statistical interpretation of bioequivalence data is a significant aspect of pharmaceutical research. Bioequivalence refers to the absence of any significant difference in the rate and extent to which the active ingredient in pharmaceutical products becomes available at the site of drug action when administered at the same molar dose under similar conditions. This helps determine if different drug products have similar absorption rates, ensuring their interchangeability.Statistical...
194
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

5.1K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
5.1K
Introduction to Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.3K
Nonparametric statistics offer a powerful alternative to traditional parametric methods, useful when assumptions about the population distribution cannot be made. Unlike parametric tests, which require data to follow a specific distribution with well-defined parameters (such as the mean and standard deviation), nonparametric tests do not require such constraints. This makes them particularly valuable when dealing with small sample sizes, skewed data, or ordinal and categorical variables.
One of...
1.3K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

5.0K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
5.0K
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K
Choosing Between z and t Distribution01:25

Choosing Between z and t Distribution

3.5K
The z and the Student t distribution estimate the population mean using the sample mean and standard deviation. However, to decide which distribution to use for a calculation, one needs to determine the sample size, the nature of the distribution, and whether the population standard deviation is known. If the population standard deviation is known and the population is normally distributed, or if the sample size is greater than 30, the z distribution is preferred. The Student t distribution is...
3.5K

You might also read

Related Articles

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

Sort by
Same author

Spatiotemporal brain-state dynamics delineate executive function subtypes in school-aged autism: evidence from co-activation patterns and a four-year follow-up.

Molecular autism·2026
Same author

Ten-Year Cost Projections for Medicare Beneficiaries 65 Years or Older With HIV.

JAMA network open·2026
Same author

NSD2 Degradation Remediates the Oncogenic Cistrome in t(4;14) Multiple Myeloma.

Blood·2026
Same author

Anticoagulation in Device-Detected Atrial Fibrillation-Uncertainty and Heterogeneity in Value.

JAMA network open·2026
Same author

Frankenstein or My Fair Lady? Lessons on Participatory Governance from Oregon Medicaid's Priority-Setting Experiment.

Journal of health politics, policy and law·2026
Same author

JAKPOT Prediction Rule for Erythrocytosis: External Validation and Cost-Effectiveness Analysis.

American journal of hematology·2026

Related Experiment Video

Updated: Jan 15, 2026

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

16.0K

A Threshold Inequality Aversion Parameter Approach to Interpret Distributional Cost-Effectiveness Analysis Results.

Ankur Pandya1, Jinyi Zhu2, Andrea Luviano3

  • 1Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|October 15, 2025
PubMed
Summary

Threshold inequality aversion parameter (TIAP) values can help overcome challenges in using distributional cost-effectiveness analysis (DCEA) for health equity. Reporting TIAPs enables practical application of DCEA by providing interpretable thresholds for decision-making.

Keywords:
cost-effectiveness analysisdistributional cost-effectiveness analysisequity impact analysis

More Related Videos

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.2K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

Related Experiment Videos

Last Updated: Jan 15, 2026

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

16.0K
Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education
09:00

Author Spotlight: Validation of SICOLE-R for Assessing Cognitive and Reading Skills in Spanish-Speaking Children and Its Role in Personalized Education

Published on: August 16, 2024

1.2K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.9K

Area of Science:

  • Health economics
  • Decision science
  • Public health policy

Background:

  • Distributional cost-effectiveness analysis (DCEA) is crucial for evaluating health interventions considering equity.
  • Calculating the equally distributed equivalent (EDE) requires an inequality aversion parameter, often unknown in practice.
  • This limits the application of DCEA in real-world health disparity contexts.

Purpose of the Study:

  • To propose and validate threshold inequality aversion parameter (TIAP) values for DCEA.
  • To enable practical interpretation and application of DCEA findings.
  • To facilitate informed decision-making in health resource allocation.

Main Methods:

  • Developed methods for calculating TIAPs in pairwise and multistrategy DCEAs.
  • TIAPs are estimated by equating the EDEs of competing strategies.
  • Interpretation relies on defining lower and upper bounds of the inequality aversion parameter range (LBIAR and UBIAR).

Main Results:

  • In pairwise DCEA, TIAPs indicate preference for equity-improving or cost-effective strategies based on comparison with LBIAR and UBIAR.
  • TIAPs falling within the LBIAR-UBIAR range require further contextual analysis for optimal strategy selection.
  • The proposed TIAP method offers a practical approach to DCEA interpretation.

Conclusions:

  • TIAP interpretation parallels the use of incremental cost-effectiveness ratios (ICERs) in cost-effectiveness analysis.
  • Reporting TIAPs can significantly enhance the practical utility and widespread adoption of DCEA.
  • Further research on inequality aversion parameters is needed, but TIAPs offer a viable path forward.