Aggregating multiple test results to improve medical decision-making.
Gathering observational data for medical decision-making often involves uncertainties arising from both type I (false positive) and type II (false negative) errors. In this work, we develop a statistical model to study how medical decision-making can be improved by aggregating results from repeated...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012749 |
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| _version_ | 1832540357470978048 |
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| author | Lucas Böttcher Maria R D'Orsogna Tom Chou |
| author_facet | Lucas Böttcher Maria R D'Orsogna Tom Chou |
| author_sort | Lucas Böttcher |
| collection | DOAJ |
| description | Gathering observational data for medical decision-making often involves uncertainties arising from both type I (false positive) and type II (false negative) errors. In this work, we develop a statistical model to study how medical decision-making can be improved by aggregating results from repeated diagnostic and screening tests. Our approach is relevant to not only clinical settings such as medical imaging, but also to public health, as highlighted by the need for rapid, cost-effective testing methods during the SARS-CoV-2 pandemic. Our model enables the development of testing protocols with an arbitrary number of tests, which can be customized to meet requirements for type I and type II errors. This allows us to adjust sensitivity and specificity according to application-specific needs. Additionally, we derive generalized Rogan-Gladen estimates of disease prevalence that account for an arbitrary number of tests with potentially different type I and type II errors. We also provide the corresponding uncertainty quantification. |
| format | Article |
| id | doaj-art-e3658e5c03cd4e80a0104088aedc7f3f |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-e3658e5c03cd4e80a0104088aedc7f3f2025-02-05T05:30:39ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-01-01211e101274910.1371/journal.pcbi.1012749Aggregating multiple test results to improve medical decision-making.Lucas BöttcherMaria R D'OrsognaTom ChouGathering observational data for medical decision-making often involves uncertainties arising from both type I (false positive) and type II (false negative) errors. In this work, we develop a statistical model to study how medical decision-making can be improved by aggregating results from repeated diagnostic and screening tests. Our approach is relevant to not only clinical settings such as medical imaging, but also to public health, as highlighted by the need for rapid, cost-effective testing methods during the SARS-CoV-2 pandemic. Our model enables the development of testing protocols with an arbitrary number of tests, which can be customized to meet requirements for type I and type II errors. This allows us to adjust sensitivity and specificity according to application-specific needs. Additionally, we derive generalized Rogan-Gladen estimates of disease prevalence that account for an arbitrary number of tests with potentially different type I and type II errors. We also provide the corresponding uncertainty quantification.https://doi.org/10.1371/journal.pcbi.1012749 |
| spellingShingle | Lucas Böttcher Maria R D'Orsogna Tom Chou Aggregating multiple test results to improve medical decision-making. PLoS Computational Biology |
| title | Aggregating multiple test results to improve medical decision-making. |
| title_full | Aggregating multiple test results to improve medical decision-making. |
| title_fullStr | Aggregating multiple test results to improve medical decision-making. |
| title_full_unstemmed | Aggregating multiple test results to improve medical decision-making. |
| title_short | Aggregating multiple test results to improve medical decision-making. |
| title_sort | aggregating multiple test results to improve medical decision making |
| url | https://doi.org/10.1371/journal.pcbi.1012749 |
| work_keys_str_mv | AT lucasbottcher aggregatingmultipletestresultstoimprovemedicaldecisionmaking AT mariardorsogna aggregatingmultipletestresultstoimprovemedicaldecisionmaking AT tomchou aggregatingmultipletestresultstoimprovemedicaldecisionmaking |