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Related Concept Videos

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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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.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Confidence Intervals01:21

Confidence Intervals

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An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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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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Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Improved confidence intervals of a small probability from pooled testing with misclassification.

Chunling Liu1, Aiyi Liu2, Bo Zhang3

  • 1Department of Applied Mathematics, Hong Kong Polytechnic University , Hong Kong , PR China.

Frontiers in Public Health
|December 19, 2013
PubMed
Summary

This study evaluates confidence intervals for rare disease prevalence using pooled testing with imperfect biomarkers. Standard methods are inefficient; alternative approaches offer better accuracy for disease surveillance.

Keywords:
confidence intervalscoverage probabilityexact inferencepoolingprevalencerare eventsensitivityspecificity

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Diagnostics

Background:

  • Rare disease prevalence estimation is crucial for public health.
  • Dorfman's pooled testing offers efficiency but requires careful statistical analysis.
  • Imperfect biomarkers introduce classification error, complicating prevalence estimation.

Purpose of the Study:

  • To construct accurate confidence intervals for rare disease prevalence using Dorfman's pooled testing.
  • To evaluate the performance of confidence intervals when disease status is determined by an imperfect biomarker.
  • To compare the efficiency of standard methods versus proposed alternatives.

Main Methods:

  • Utilized Dorfman's pooled testing procedure for group testing.
  • Derived confidence intervals by converting intervals for the probability of a positive test within a pool.
  • Investigated Wald confidence intervals based on normal approximation.
  • Proposed and analyzed alternative confidence interval methods.

Main Results:

  • Normal approximation-based Wald confidence intervals demonstrate inefficiency in coverage probability.
  • The inefficiency persists even with a large number of tested pools.
  • Proposed alternative methods show improved performance regarding coverage probability and interval length.

Conclusions:

  • Standard confidence intervals are inadequate for rare disease prevalence estimation with pooled testing and imperfect biomarkers.
  • Alternative statistical methods are necessary for reliable estimation in such scenarios.
  • The choice of confidence interval method significantly impacts the accuracy of rare disease prevalence estimates.