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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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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...
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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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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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Interpretation of Confidence Intervals01:19

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
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AppRaise: Software for Quantifying Evidence Uncertainty in Systematic Reviews Using a Posterior Mixture Model.

Conrad Kabali1,2

  • 1Health Technology Assessment Unit, Acute and Hospital-Based Care Portfolio, Ontario Health, Toronto, Ontario, Canada.

Journal of Evaluation in Clinical Practice
|September 10, 2025
PubMed
Summary

This study introduces AppRaise, a free software tool that quantifies biases in systematic reviews. AppRaise enhances evidence-based healthcare by providing a unified measure of uncertainty for better decision-making.

Keywords:
AppRaisedecision‐makinghealth technology assessmentposterior mixture modelquantifying uncertaintysystematic review

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Area of Science:

  • Medical Informatics
  • Biostatistics
  • Health Technology Assessment

Background:

  • Systematic reviews are crucial for evidence-based healthcare, but quantifying biases remains challenging.
  • Current methods often rely on narrative assessments, lacking quantitative rigor.
  • Time constraints and technical difficulties hinder comprehensive bias assessment by evidence appraisers.

Purpose of the Study:

  • To develop a quantitative approach for assessing biases and random errors in systematic reviews.
  • To introduce AppRaise, a free, web-based software implementing a posterior mixture model for bias assessment.

Main Methods:

  • A posterior mixture model was developed to integrate random errors and biases into a unified uncertainty measure.
  • The AppRaise software was created to provide user-friendly access to this quantitative approach.
  • The method was applied to a health technology assessment (HTA) report on continuous glucose monitoring (CGM).

Main Results:

  • Application of AppRaise to the HTA report showed an 86% probability that CGM reduces A1c levels in type 1 diabetes.
  • Results were comparable to other quantitative bias-adjusted approaches in systematic reviews.
  • The software facilitated a high level of certainty in the findings.

Conclusions:

  • AppRaise offers a valuable tool for validating evidence quality and assessing sensitivity to bias in systematic reviews.
  • It can be used independently or to complement qualitative scoring methods.
  • The software aids in robust decision-making for evidence-based healthcare.