ArticleStatistics in medicine2022
Bayesian sample size determination for diagnostic accuracy studies.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Article
- Vascular invasion-associated gene expression is detectable in pre-surgical biopsies of stage I lung adenocarcinoma.Nature communications · 2026Article
- Interpreting frequentist hypothesis tests: insights from Bayesian inference.Canadian journal of anaesthesia = Journal canadien d'anesthesie · 2023Article
- Sample size determination for point-of-care COVID-19 diagnostic tests: a Bayesian approach.Diagnostic and prognostic research · 2023Article
- Exosomal circ-0020887 and circ-0009590 as novel biomarkers for the diagnosis and prediction of short-term adverse cardiovascular outcomes in STEMI patients.Open medicine (Warsaw, Poland) · 2023Article
- Bayesian sample size determination for diagnostic accuracy studies.Statistics in medicine · 2022Article
- Bayesian Power-Based Sample Size Determination for Diagnostic Accuracy Studies.Pharmaceutical statisticsArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
The development of a new diagnostic test ideally follows a sequence of stages which, among other aims, evaluate technical performance. This includes an analytical validity study, a diagnostic accuracy study, and an interventional clinical utility study. In this article, we propose a novel Bayesian approach to sample size determination for the diagnostic accuracy study, which takes advantage of information available from the analytical validity stage. We utilize assurance to calculate the required sample size based on the target width of a posterior probability interval and can choose to use or disregard the data from the analytical validity study when subsequently inferring measures of test accuracy. Sensitivity analyses are performed to assess the robustness of the proposed sample size to the choice of prior, and prior-data conflict is evaluated by comparing the data to the prior predictive distributions. We illustrate the proposed approach using a motivating real-life application involving a diagnostic test for ventilator associated pneumonia. Finally, we compare the properties of the approach against commonly used alternatives. The results show that, when suitable prior information is available, the assurance-based approach can reduce the required sample size when compared to alternative approaches.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.