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The Uncertainty Principle04:08

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Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...
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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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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...
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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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Multivariable and Structural Uncertainty Analyses for Cost-Effectiveness Estimates: Back to the Future.

Josephine Mauskopf1

  • 1RTI Health Solutions, Research Triangle Park, NC, USA.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|May 21, 2019
PubMed
Summary
This summary is machine-generated.

Uncertainty analysis methods for cost-effectiveness estimates have evolved significantly over 20 years. This evolution, particularly in structural uncertainty analysis, aims to provide more useful information for health decision-makers.

Keywords:
multivariableprobabilistic sensitivity analysisstructuraluncertainty analysis

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Area of Science:

  • Health economics
  • Decision science
  • Health technology assessment

Background:

  • Commentary celebrates 20 years of Value in Health.
  • Reviews evolution of methodological literature for uncertainty analysis in cost-effectiveness estimates.
  • Focuses on multivariable and structural uncertainty analysis.

Purpose of the Study:

  • Illustrate the impact of uncertainty analysis guidelines.
  • Show changes in uncertainty analysis inclusion in cost-effectiveness analyses over 20 years.
  • Examine publications from 1999/2000, 2007, and 2017.

Main Methods:

  • Review of methodological literature on uncertainty analysis.
  • Analysis of publications in Value in Health from specific years (1999/2000, 2007, 2017).
  • Thematic organization into past, present, and future of uncertainty analysis.

Main Results:

  • Past: Development and use of multivariable uncertainty analysis methods.
  • Present: Growing awareness and implementation of structural uncertainty analysis.
  • Future: Exploration of combined multivariable and structural uncertainty analysis methods.

Conclusions:

  • Continued evolution of uncertainty analyses is crucial.
  • Enhanced uncertainty analyses should prioritize providing useful information to decision-makers.
  • Implications for health technology assessment submissions and published studies.