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Related Concept Videos

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.
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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 't,' or...
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...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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...
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...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

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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Related Experiment Videos

A framework for addressing structural uncertainty in decision models.

Christopher H Jackson1, Laura Bojke2, Simon G Thompson1

  • 1MRC Biostatistics Unit, Cambridge, UK (CHJ, SGT, LDS)

Medical Decision Making : an International Journal of the Society for Medical Decision Making
|May 24, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a framework to formally address structural uncertainties in health technology assessment models. It quantizes all structural choices, improving cost-effectiveness estimates for better healthcare decisions.

Related Experiment Videos

Area of Science:

  • Health Economics
  • Decision Analysis
  • Biostatistics

Background:

  • Decision analytic models in health technology assessment (HTA) face uncertainties.
  • Probabilistic quantification addresses parameter uncertainties, but structural assumptions are often explored informally.
  • A formal framework is needed to integrate all structural uncertainties.

Purpose of the Study:

  • To present a framework for formally accounting for structural uncertainties in decision analytic models.
  • To expand models to include parameters representing all structural choices.
  • To demonstrate methods for estimating uncertainty when data are limited or absent.

Main Methods:

  • Expanded decision models to incorporate parameters for all structural choices.
  • Quantified uncertainty arising from imprecise parameter estimation or lack of data.
  • Used model averaging weighted by predictive ability against data.
  • Incorporated expert belief elicitation for data-poor scenarios.

Main Results:

  • Demonstrated a method to formally integrate structural uncertainties into HTA models.
  • Showcased application in decision models for antiplatelet therapies and psoriatic arthritis treatments.
  • Provided a pathway for more robust cost-effectiveness estimations.

Conclusions:

  • The proposed framework formally addresses structural uncertainties, enhancing HTA model reliability.
  • This approach improves the quantification of cost-effectiveness, especially with limited data.
  • Facilitates more informed healthcare technology and policy decisions.