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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/12/11/2839 |
| _version_ | 1797465595244969984 |
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| author | Wan Nor Arifin Umi Kalsom Yusof |
| author_facet | Wan Nor Arifin Umi Kalsom Yusof |
| author_sort | Wan Nor Arifin |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T18:23:45Z |
| format | Article |
| id | doaj.art-be853f2d07a54507983c583b056e6b22 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T18:23:45Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-be853f2d07a54507983c583b056e6b222023-11-24T08:05:07ZengMDPI AGDiagnostics2075-44182022-11-011211283910.3390/diagnostics12112839Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic TestsWan Nor Arifin0Umi Kalsom Yusof1School of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, MalaysiaSchool of Computer Sciences, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, MalaysiaIn 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.https://www.mdpi.com/2075-4418/12/11/2839correction methoddiagnostic testinverse probability bootstrap samplingpartial verification biaspropensity score |
| spellingShingle | Wan Nor Arifin Umi Kalsom Yusof Partial Verification Bias Correction Using Inverse Probability Bootstrap Sampling for Binary Diagnostic Tests Diagnostics correction method diagnostic test inverse probability bootstrap sampling partial verification bias propensity score |
| 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 | correction method diagnostic test inverse probability bootstrap sampling partial verification bias propensity score |
| url | https://www.mdpi.com/2075-4418/12/11/2839 |
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