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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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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,...
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Uncertainty: Confidence Intervals00:54

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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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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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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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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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...
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
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Updated: Jul 30, 2025

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements
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Real-World Cost-Effectiveness Analysis: How Much Uncertainty Is in the Results?

Heather K Barr1, Andrea M Guggenbickler1, Jeffrey S Hoch1,2,3

  • 1Graduate Group in Public Health Sciences, Department of Public Health Sciences, University of California, Davis, CA 95616, USA.

Current Oncology (Toronto, Ont.)
|May 15, 2023
PubMed
Summary

Statistical uncertainty in cost-effectiveness analyses (CEA) of new cancer treatments is crucial for healthcare decisions. This study reviews methods to present uncertainty, finding scatterplots most common, with many analyses showing significant uncertainty.

Keywords:
cancercancer interventionscost effectivenesseconomic evaluationhealthcarereal-world interventionsstatisticsuncertainty

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Area of Science:

  • Health Economics
  • Oncology
  • Biostatistics

Background:

  • Cost-effectiveness analyses (CEA) of novel cancer treatments are vital for healthcare investment decisions.
  • Real-world CEA results often incorporate statistical uncertainty, impacting interpretation.

Purpose of the Study:

  • To identify and evaluate methods for characterizing statistical uncertainty in real-world CEA of cancer treatments.
  • To assess the prevalence and application of these uncertainty methods in recent literature.

Main Methods:

  • Literature review of 22 articles on real-world CEA for novel cancer treatments.
  • Identification and categorization of five methods for presenting statistical uncertainty: 95% confidence intervals (CI) for incremental cost-effectiveness ratio (ICER) and incremental net benefit (INB), INB by willingness-to-pay (WTP) plots, cost-effectiveness acceptability curves (CEAC), and cost-effectiveness scatterplots.
  • Analysis of the usage and implications of these methods in the reviewed articles.

Main Results:

  • Seventy-seven percent of reviewed articles presented results on statistical uncertainty.
  • Cost-effectiveness scatterplots were the most frequently used method (34.3%).
  • Forty percent of analyses using scatterplots indicated high levels of statistical uncertainty, suggesting potential for different conclusions.

Conclusions:

  • Understanding and effectively communicating statistical uncertainty in real-world CEA is essential for accurate interpretation.
  • Improved knowledge translation regarding uncertainty can enhance healthcare efficiency and patient outcomes.