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Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests

In medical care, it is important to evaluate any new diagnostic test in the form of diagnostic accuracy studies. These new tests are compared to gold standard tests, where the performance of binary diagnostic tests is usually measured by sensitivity (Sn) and specificity (Sp). However, these accuracy...

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Autores principales: Arifin, Wan Nor, Yusof, Umi Kalsom
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689704/
https://www.ncbi.nlm.nih.gov/pubmed/36428900
http://dx.doi.org/10.3390/diagnostics12112839
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author Arifin, Wan Nor
Yusof, Umi Kalsom
author_facet Arifin, Wan Nor
Yusof, Umi Kalsom
author_sort Arifin, Wan Nor
collection PubMed
description In medical care, it is important to evaluate any new diagnostic test in the form of diagnostic accuracy studies. These new tests are compared to gold standard tests, where the performance of binary diagnostic tests is usually measured by sensitivity (Sn) and specificity (Sp). However, these accuracy measures are often biased owing to selective verification of the patients, known as partial verification bias (PVB). Inverse probability bootstrap (IPB) sampling is a general method to correct sampling bias in model-based analysis and produces debiased data for analysis. However, its utility in PVB correction has not been investigated before. The objective of this study was to investigate IPB in the context of PVB correction under the missing-at-random assumption for binary diagnostic tests. IPB was adapted for PVB correction, and tested and compared with existing methods using simulated and clinical data sets. The results indicated that IPB is accurate for Sn and Sp estimation as it showed low bias. However, IPB was less precise than existing methods as indicated by the higher standard error (SE). Despite this issue, it is recommended to use IPB when subsequent analysis with full data analytic methods is expected. Further studies must be conducted to reduce the SE.
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spelling pubmed-96897042022-11-25 Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests Arifin, Wan Nor Yusof, Umi Kalsom Diagnostics (Basel) Article In medical care, it is important to evaluate any new diagnostic test in the form of diagnostic accuracy studies. These new tests are compared to gold standard tests, where the performance of binary diagnostic tests is usually measured by sensitivity (Sn) and specificity (Sp). However, these accuracy measures are often biased owing to selective verification of the patients, known as partial verification bias (PVB). Inverse probability bootstrap (IPB) sampling is a general method to correct sampling bias in model-based analysis and produces debiased data for analysis. However, its utility in PVB correction has not been investigated before. The objective of this study was to investigate IPB in the context of PVB correction under the missing-at-random assumption for binary diagnostic tests. IPB was adapted for PVB correction, and tested and compared with existing methods using simulated and clinical data sets. The results indicated that IPB is accurate for Sn and Sp estimation as it showed low bias. However, IPB was less precise than existing methods as indicated by the higher standard error (SE). Despite this issue, it is recommended to use IPB when subsequent analysis with full data analytic methods is expected. Further studies must be conducted to reduce the SE. MDPI 2022-11-17 /pmc/articles/PMC9689704/ /pubmed/36428900 http://dx.doi.org/10.3390/diagnostics12112839 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Arifin, Wan Nor
Yusof, Umi Kalsom
Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests
title Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests
title_full Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests
title_fullStr Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests
title_full_unstemmed Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests
title_short Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests
title_sort partial verification bias correction using inverse probability bootstrap sampling for binary diagnostic tests
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9689704/
https://www.ncbi.nlm.nih.gov/pubmed/36428900
http://dx.doi.org/10.3390/diagnostics12112839
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