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An efficient confidence interval estimation for prevalence calculated from misclassified data

An efficient confidence interval estimation for prevalence calculated from misclassified data The estimation of prevalence using a screening tool is done frequently in epidemiology research. The tools used for the estimation are usually associated with a certain level of misclassification. Additional adjustments are required to eliminate the bias in the prevalence and the confidence interval (CI) estimate. A frequently used method for this correction is by modifying the upper and lower limits, using sensitivity and specificity, increasing the width of the CI. The issue is exaggerated with a minimal sample size. Zhou and Li recently developed a method to estimate the CI using the Edgeworth expansion of the logit transformed binomial proportion. This article introduces a specialised tool by re-estimating the confidence limits adjusting for misclassified measurements, and assesses their characteristics through a simulation. The paper provides evidence that the re-estimated new interval performs better in the presence of misclassification. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biostatistics & Epidemiology Taylor & Francis

An efficient confidence interval estimation for prevalence calculated from misclassified data

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An efficient confidence interval estimation for prevalence calculated from misclassified data

Abstract

The estimation of prevalence using a screening tool is done frequently in epidemiology research. The tools used for the estimation are usually associated with a certain level of misclassification. Additional adjustments are required to eliminate the bias in the prevalence and the confidence interval (CI) estimate. A frequently used method for this correction is by modifying the upper and lower limits, using sensitivity and specificity, increasing the width of the CI. The issue is exaggerated...
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Publisher
Taylor & Francis
Copyright
© 2022 International Biometric Society – Chinese Region
ISSN
2470-9379
eISSN
2470-9360
DOI
10.1080/24709360.2022.2076530
Publisher site
See Article on Publisher Site

Abstract

The estimation of prevalence using a screening tool is done frequently in epidemiology research. The tools used for the estimation are usually associated with a certain level of misclassification. Additional adjustments are required to eliminate the bias in the prevalence and the confidence interval (CI) estimate. A frequently used method for this correction is by modifying the upper and lower limits, using sensitivity and specificity, increasing the width of the CI. The issue is exaggerated with a minimal sample size. Zhou and Li recently developed a method to estimate the CI using the Edgeworth expansion of the logit transformed binomial proportion. This article introduces a specialised tool by re-estimating the confidence limits adjusting for misclassified measurements, and assesses their characteristics through a simulation. The paper provides evidence that the re-estimated new interval performs better in the presence of misclassification.

Journal

Biostatistics & Epidemiology – Taylor & Francis

Published: Jul 6, 2022

Keywords: Binomial proportion; screening tool; Edgeworth expansions; confidence limits

References

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