Sample size determination for point-of-care COVID-19 diagnostic tests: a Bayesian approach.

Autor: Williamson SF; Biostatistics Research Group, Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK. faye.williamson@newcastle.ac.uk., Williams CJ; NIHR Newcastle In Vitro Diagnostic Cooperative, Newcastle University, Newcastle upon Tyne, UK., Lendrem BC; NIHR Newcastle In Vitro Diagnostic Cooperative, Newcastle University, Newcastle upon Tyne, UK., Wilson KJ; School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK.
Jazyk: angličtina
Zdroj: Diagnostic and prognostic research [Diagn Progn Res] 2023 Aug 18; Vol. 7 (1), pp. 17. Date of Electronic Publication: 2023 Aug 18.
DOI: 10.1186/s41512-023-00153-1
Abstrakt: Background: In a pandemic setting, it is critical to evaluate and deploy accurate diagnostic tests rapidly. This relies heavily on the sample size chosen to assess the test accuracy (e.g. sensitivity and specificity) during the diagnostic accuracy study. Too small a sample size will lead to imprecise estimates of the accuracy measures, whereas too large a sample size may delay the development process unnecessarily. This study considers use of a Bayesian method to guide sample size determination for diagnostic accuracy studies, with application to COVID-19 rapid viral detection tests. Specifically, we investigate whether utilising existing information (e.g. from preceding laboratory studies) within a Bayesian framework can reduce the required sample size, whilst maintaining test accuracy to the desired precision.
Methods: The method presented is based on the Bayesian concept of assurance which, in this context, represents the unconditional probability that a diagnostic accuracy study yields sensitivity and/or specificity intervals with the desired precision. We conduct a simulation study to evaluate the performance of this approach in a variety of COVID-19 settings, and compare it to commonly used power-based methods. An accompanying interactive web application is available, which can be used by researchers to perform the sample size calculations.
Results: Results show that the Bayesian assurance method can reduce the required sample size for COVID-19 diagnostic accuracy studies, compared to standard methods, by making better use of laboratory data, without loss of performance. Increasing the size of the laboratory study can further reduce the required sample size in the diagnostic accuracy study.
Conclusions: The method considered in this paper is an important advancement for increasing the efficiency of the evidence development pathway. It has highlighted that the trade-off between lab study sample size and diagnostic accuracy study sample size should be carefully considered, since establishing an adequate lab sample size can bring longer-term gains. Although emphasis is on its use in the COVID-19 pandemic setting, where we envisage it will have the most impact, it can be usefully applied in other clinical areas.
(© 2023. BioMed Central Ltd., part of Springer Nature.)
Databáze: MEDLINE
Externí odkaz: