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Related Concept Videos

Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
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Bandpass Sampling

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In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
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Sampling Theorem01:15

Sampling Theorem

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In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
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Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Optimal two-phase sampling for estimating the area under the receiver operating characteristic curve.

Yougui Wu1

  • 1Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, Florida, USA.

Statistics in Medicine
|November 19, 2020
PubMed
Summary

This study introduces an optimal two-phase sampling design to improve the accuracy of estimating the area under the receiver operating characteristic curve (AUC). The new method significantly reduces variance by over-sampling subjects with extreme test results.

Keywords:
area under a ROC curveone-phase random samplingoptimal sampling probabilitiesrelative efficiencytwo-phase sampling

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Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Epidemiology

Background:

  • Estimating the area under the receiver operating characteristic curve (AUC) is crucial for evaluating diagnostic test performance.
  • Existing two-phase sampling methods for AUC estimation do not optimize sampling probabilities for variance reduction.

Purpose of the Study:

  • To develop an optimal two-phase sampling design for evaluating ordinal tests in disease classification.
  • To derive an analytic variance formula for the AUC estimator to determine optimal sampling probabilities.

Main Methods:

  • Derivation of an analytic variance formula for the AUC estimator in a two-phase sampling design.
  • Optimization of sampling probabilities to minimize AUC estimator variance.
  • Simulation studies to compare the efficiency of optimal allocation (OA) versus proportional allocation (PA) and one-phase random sampling.

Main Results:

  • Two-phase sampling under OA significantly reduces variance compared to PA, particularly with over-sampling of subjects with low and high ordinal test levels.
  • Both OA and PA two-phase sampling offer advantages over one-phase random sampling in reducing AUC estimator variance when diagnostic test result variances differ between disease and non-disease populations.
  • The optimal two-phase sampling design was successfully applied to a real-world example for screening childhood asthma using a questionnaire score.

Conclusions:

  • The proposed optimal two-phase sampling design provides a statistically efficient approach for estimating AUC.
  • This method offers substantial variance reduction, improving the reliability of diagnostic test performance evaluation.
  • The findings have practical implications for designing studies evaluating diagnostic accuracy, especially for ordinal tests.