Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

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

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...
Loading next page...
 
/lp/taylor-francis/an-efficient-confidence-interval-estimation-for-prevalence-calculated-NsPJcjZ5IZ
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 & EpidemiologyTaylor & Francis

Published: Jul 6, 2022

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

References