A Bayesian Estimator of Sample Size Article Swipe
We consider a Bayesian estimator of sample size (BESS) and an application to oncology dose optimization clinical trials. BESS is built upon three pillars, Sample size, Evidence from observed data, and Confidence in posterior inference. It uses a simple logic of "given the evidence from data, a specific sample size can achieve a degree of confidence in the posterior inference." The key distinction between BESS and standard sample size estimation (SSE) is that SSE, typically based on Frequentist inference, specifies the true parameters values in its calculation while BESS assumes possible outcome from the observed data. As a result, the calibration of the sample size is not based on type I or type II error rates, but on posterior probabilities. We demonstrate that BESS leads to a more interpretable statement for investigators, and can easily accommodates prior information as well as sample size re-estimation. We explore its performance in comparison to the standard SSE and demonstrate its usage through a case study of oncology optimization trial. BESS can be applied to general hypothesis tests. An R tool is available at https://ccte.uchicago.edu/BESS.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2404.07923
- https://arxiv.org/pdf/2404.07923
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394782217
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4394782217Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2404.07923Digital Object Identifier
- Title
-
A Bayesian Estimator of Sample SizeWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-04-11Full publication date if available
- Authors
-
Dehua Bi, Yuan JiList of authors in order
- Landing page
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https://arxiv.org/abs/2404.07923Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2404.07923Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2404.07923Direct OA link when available
- Concepts
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Frequentist inference, Sample size determination, Estimator, Statistics, Bayesian probability, Inference, Posterior probability, Confidence interval, Type I and type II errors, Statistical inference, Computer science, Bayesian inference, Mathematics, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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