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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Decision Making: P-value Method01:09

Decision Making: P-value Method

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 have a...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Uncertainty: Overview00:59

Uncertainty: Overview

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.
Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...

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An R-Based Landscape Validation of a Competing Risk Model
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Published on: September 16, 2022

Accounting for methodological, structural, and parameter uncertainty in decision-analytic models: a practical guide.

Joke Bilcke1, Philippe Beutels1, Marc Brisson2,3

  • 1Center for Health Economic Research and Modeling for Infectious Diseases (CHERMID), Vaccine and Infectious Disease Institute (Vaxinfectio), Antwerp University, Antwerp, Belgium (JB, PB)

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|June 10, 2011
PubMed
Summary
This summary is machine-generated.

This guide provides a practical approach to incorporating various uncertainties into decision-analytic models. It offers a checklist to assess how well uncertainty is handled, improving model reliability for decision-making.

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Area of Science:

  • Decision Analysis
  • Health Technology Assessment
  • Modeling and Simulation

Background:

  • Uncertainty analysis is standard in decision-analytic modeling but often limited in scope.
  • Techniques exist to present the impact of methodological, structural, and parameter uncertainty.
  • Integrating diverse uncertainty types into a single model remains a challenge.

Purpose of the Study:

  • To present a step-by-step guide for an integrated approach to accounting for different uncertainties in decision-analytic models.
  • To provide a checklist for assessing the incorporation of uncertainty in models.
  • To facilitate the mainstream adoption of advanced uncertainty analysis techniques.

Main Methods:

  • Development of an integrated, step-by-step guide for uncertainty analysis.
  • Creation of a checklist for evaluating uncertainty incorporation in models.
  • Description of methods for identifying key sources of uncertainty impacting results.

Main Results:

  • The guide offers a structured approach to incorporating multiple uncertainty types within a single model.
  • The checklist aids in assessing the thoroughness of uncertainty analysis.
  • Methods are described for handling challenging situations like limited resources or unreliable evidence.

Conclusions:

  • The guide and checklist can enhance the rigor of decision-analytic modeling by improving uncertainty assessment.
  • These tools support both analysts in conducting more comprehensive analyses and decision-makers in evaluating model trustworthiness.
  • Wider adoption of these techniques can lead to more robust and reliable decision-making based on model outputs.