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Related Concept Videos

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.
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Confidence Interval for Estimating Population Mean01:25

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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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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.
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Prediction Intervals01:03

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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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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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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An R-Based Landscape Validation of a Competing Risk Model
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Joint confidence region estimation on predictive values.

Braydon J Schaible1, Jingjing Yin1

  • 1Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA.

Pharmaceutical Statistics
|May 22, 2021
PubMed
Summary

This study introduces new methods for estimating positive predictive value (PPV) and negative predictive value (NPV) for continuous diagnostic tests. These approaches improve accuracy and confidence intervals compared to existing methods.

Keywords:
Box-Cox transformationYouden indexjoint confidence regionpredictive value

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Area of Science:

  • Biostatistics
  • Diagnostic accuracy research
  • Medical decision making

Background:

  • Sensitivity and specificity are standard measures for continuous diagnostic tests, but rely on estimated cut-offs.
  • Positive predictive value (PPV) and negative predictive value (NPV) offer a more clinically relevant post-test probability.
  • Existing methods for PPV/NPV inference often overlook their inherent correlation and assume binary test results.

Purpose of the Study:

  • To develop novel statistical approaches for joint inference of PPV and NPV for continuous diagnostic tests.
  • To provide accurate confidence intervals for PPV and NPV that account for their correlation.
  • To compare the performance of proposed methods against existing techniques using simulation and real-world data.

Main Methods:

  • Proposed several statistical approaches for estimating the joint confidence region of PPV and NPV.
  • Developed methods to estimate individual confidence intervals for PPV and NPV simultaneously.
  • Utilized simulation studies with normal and non-normal data distributions to evaluate performance.

Main Results:

  • The proposed methods demonstrated satisfactory coverage probabilities for both normal and non-normal data.
  • New approaches outperformed existing methods, yielding improved coverage and narrower confidence intervals for PPV and NPV.
  • The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was used for practical illustration and comparison.

Conclusions:

  • The novel joint inference methods provide a more robust and accurate assessment of diagnostic test performance for continuous biomarkers.
  • These improved methods enhance the reliability of predictive values in clinical decision-making.
  • The study highlights the importance of accounting for the correlation between PPV and NPV in diagnostic accuracy evaluations.