A Bayesian Sample Size Estimation Procedure Based on a B-Splines Semiparametric Elicitation Method

Danila Azzolina, Paola Berchialla, Silvia Bressan, Liviana Da Dalt, Dario Gregori and Ileana Baldi ()
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Danila Azzolina: Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35122 Padua, Italy
Paola Berchialla: Department of Clinical and Biological Sciences, University of Torino, 10124 Turin, Italy
Silvia Bressan: Department of Pediatrics, University of Padova, 35122 Padua, Italy
Liviana Da Dalt: Department of Pediatrics, University of Padova, 35122 Padua, Italy
Dario Gregori: Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35122 Padua, Italy
Ileana Baldi: Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac Thoracic Vascular Sciences and Public Health, University of Padova, 35122 Padua, Italy

IJERPH, 2022, vol. 19, issue 21, 1-15

Abstract: Sample size estimation is a fundamental element of a clinical trial, and a binomial experiment is the most common situation faced in clinical trial design. A Bayesian method to determine sample size is an alternative solution to a frequentist design, especially for studies conducted on small sample sizes. The Bayesian approach uses the available knowledge, which is translated into a prior distribution, instead of a point estimate, to perform the final inference. This procedure takes the uncertainty in data prediction entirely into account. When objective data, historical information, and literature data are not available, it may be indispensable to use expert opinion to derive the prior distribution by performing an elicitation process. Expert elicitation is the process of translating expert opinion into a prior probability distribution. We investigated the estimation of a binomial sample size providing a generalized version of the average length, coverage criteria, and worst outcome criterion. The original method was proposed by Joseph and is defined in a parametric framework based on a Beta-Binomial model. We propose a more flexible approach for binary data sample size estimation in this theoretical setting by considering parametric approaches (Beta priors) and semiparametric priors based on B-splines.

Keywords: Bayesian trial; semiparametric; elicitation; sample size; phase II (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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