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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

709
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...
709
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

201
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,...
201
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

110
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
110
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

7.0K
The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
7.0K
Decision Making: P-value Method01:09

Decision Making: P-value Method

5.9K
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...
5.9K
Uncertainty: Overview00:59

Uncertainty: Overview

1.2K
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
1.2K

You might also read

Related Articles

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

Sort by
Same author

On Representations and Quantifications of Uncertainty.

Medical decision making : an international journal of the Society for Medical Decision Making·2026
Same author

Safety and efficacy of an intrinsic antitachycardia pacing algorithm in patients from Japan and South Korea: results from a cardiac device registry in the Asia Pacific region.

Journal of medical economics·2025
Same author

Impact of perioperative diagnostic tools on clinical outcomes and cost-effectiveness in parathyroid surgery: a decision model-based analysis.

BMJ open·2024
Same author

Markov Cohort State-Transition Model: A Multinomial Distribution Representation.

Medical decision making : an international journal of the Society for Medical Decision Making·2022
Same author

An approach to quantify parameter uncertainty in early assessment of novel health technologies.

Health economics·2022
Same author

Microsimulation Model Calibration with Approximate Bayesian Computation in R: A Tutorial.

Medical decision making : an international journal of the Society for Medical Decision Making·2022

Related Experiment Video

Updated: Oct 20, 2025

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

2.3K

Probability bound analysis: A novel approach for quantifying parameter uncertainty in decision-analytic modeling and

Rowan Iskandar1,2

  • 1Center of Excellence in Decision-Analytic Modeling and Health Economics Research, Swiss Institute for Translational and Entrepreneurial Medicine (sitem-insel), Bern, Switzerland.

Statistics in Medicine
|September 16, 2021
PubMed
Summary

This study introduces probability bounds analysis (PBA) for health intervention modeling, offering a flexible way to quantify uncertainty without assuming specific probability distributions. PBA provides practical tools for decision-making with limited evidence.

Keywords:
cost-effectiveness analysisdecision-analytic modelingparameter uncertaintyprobability bound analysisprobability boxuncertainty quantification

More Related Videos

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

12.2K
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

10.4K

Related Experiment Videos

Last Updated: Oct 20, 2025

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

2.3K
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

12.2K
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

10.4K

Area of Science:

  • Decision Analysis
  • Mathematical Modeling
  • Health Economics

Background:

  • Health intervention decisions often rely on limited evidence, necessitating uncertainty quantification in mathematical models.
  • Current methods like probabilistic sensitivity analysis (PSA) require precise probability distributions for model parameters, which may not be available.
  • This limitation hinders accurate assessment of uncertainty's impact on decision-relevant outcomes.

Purpose of the Study:

  • To introduce and describe probability bounds analysis (PBA) as a novel approach for representing and propagating parameter uncertainty.
  • To provide practical tools for modelers to conduct uncertainty quantification with minimal assumptions, especially when data is constrained.
  • To enable robust decision-making in health interventions despite incomplete evidence.

Main Methods:

  • Developed probability bounds analysis (PBA) to represent parameter uncertainty using intervals (p-boxes) of cumulative distribution functions, avoiding assumptions on distribution form.
  • Derived formulas for p-boxes based on common data types (min, max, median, mean, standard deviation).
  • Described methods for propagating p-boxes through black-box mathematical models and for decision-making using PBA results.

Main Results:

  • PBA offers a flexible alternative to PSA for uncertainty quantification in mathematical models.
  • Demonstrated the utility and characteristics of PBA compared to PSA through two case studies.
  • Provided practical formulas and approaches for implementing PBA with various data constraints.

Conclusions:

  • PBA equips modelers with practical tools to perform uncertainty quantification effectively, even with limited data.
  • The approach reduces reliance on distributional assumptions, leading to more robust decision-making in health interventions.
  • This methodology enhances the reliability of mathematical models used in evidence-limited scenarios.