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 Nonparametric Statistics01:28

Introduction to Nonparametric Statistics

1.6K
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...
1.6K
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

568
Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...
568
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

8.4K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
8.4K
Decision Making: P-value Method01:09

Decision Making: P-value Method

7.1K
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...
7.1K
Variation01:19

Variation

8.3K
An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
When independent and dependent variables are plotted on a scatter plot, the slope of a line is a value that describes the rate of change between the two...
8.3K
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

9.0K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
9.0K

You might also read

Related Articles

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

Sort by
Same author

Collision Tumor of Metastatic Lung Adenocarcinoma to Lower Limb Leiomyosarcoma: Case Report and Literature Review.

International journal of surgical pathology·2026
Same author

MOVIN in ICU: An observational study of Mobilisation On Vasopressors and INotropes in intensive care.

Heart & lung : the journal of critical care·2026
Same author

Quantitative bias analysis for unmeasured confounding in unanchored population-adjusted indirect comparisons.

Research synthesis methods·2026
Same author

Letter in response to Salari M, et al. Epidemiology and clinical features of Huntington's disease in MENASA region: A systematic review and meta-analysis.

Journal of Huntington's disease·2026
Same author

Observed total and live birth prevalence of Wolf-Hirschhorn syndrome in England 2015-2020.

Clinical dysmorphology·2025
Same author

Variation in the reported prevalence of Huntington's disease: a systematic review and guide to interpretation.

Journal of neurology·2025

Related Experiment Video

Updated: Mar 19, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.6K

Efficient Value of Information Calculation Using a Nonparametric Regression Approach: An Applied Perspective.

Haitham W Tuffaha1, Mark Strong2, Louisa G Gordon1

  • 1Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia; Centre for Applied Health Economics, School of Medicine, Griffith University, Meadowbrook, Queensland, Australia.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|June 22, 2016
PubMed
Summary

A new nonparametric regression approach offers a faster and efficient method for calculating value-of-information (VOI) measures, specifically expected value of perfect parameter information (EVPPI) and expected value of sample information (EVSI), compared to traditional Monte-Carlo simulations.

Keywords:
Monte-Carlo simulationnonparametric regressionvalue of information

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

2.7K
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

11.0K

Related Experiment Videos

Last Updated: Mar 19, 2026

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
09:23

Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

Published on: August 16, 2017

8.6K
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

2.7K
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

11.0K

Area of Science:

  • Decision analysis
  • Health economics
  • Computational statistics

Background:

  • Value-of-information (VOI) analysis aids in assessing the worth of additional evidence to reduce decision uncertainty.
  • Calculating VOI measures like expected value of perfect parameter information (EVPPI) and expected value of sample information (EVSI) is computationally intensive using standard Monte-Carlo methods.
  • A novel nonparametric regression approach enables direct estimation of multiparameter EVPPI and EVSI from probabilistic sensitivity analysis samples.

Purpose of the Study:

  • To showcase the utility of the nonparametric regression approach for VOI calculations in practical scenarios.
  • To benchmark the performance of the regression method against the established Monte-Carlo simulation technique.

Main Methods:

  • The nonparametric regression approach was employed to compute EVPPI and EVSI in two distinct economic models.
  • Results were rigorously compared with those obtained using the conventional Monte-Carlo simulation method.

Main Results:

  • VOI measures calculated via both the regression and Monte-Carlo approaches yielded highly comparable values.
  • The regression-based computation demonstrated significantly faster processing times compared to the standard Monte-Carlo simulation.

Conclusions:

  • The nonparametric regression approach presents a computationally efficient and user-friendly alternative for calculating EVPPI and EVSI within economic modeling frameworks.
  • This method simplifies the estimation of crucial VOI metrics, facilitating better decision-making under uncertainty.