In Bayesian inference, prior distributions formalize preexperimental information and uncertainty on model parameters. Sometimes different sources of knowledge are available, possibly leading to divergent posterior distributions and inferences. Research has been recently devoted to the development of sample size criteria that guarantee agreement of posterior information in terms of credible intervals when multiple priors are available. In these articles, the goals of reaching consensus and evidence are typically kept separated. Adopting a Bayesian performance-based approach, the present article proposes new sample size criteria for superiority trials that jointly control the achievement of both minimal evidence and consensus, measured by appropriate functions of the posterior distributions. We develop both an average criterion and a more stringent criterion that accounts for the entire predictive distributions of the selected measures of minimal evidence and consensus. Methods are developed and illustrated via simulation for trials involving binary outcomes. A real clinical trial example on Covid-19 vaccine data is presented.

Joint control of consensus and evidence in Bayesian design of clinical trials / De Santis, Fulvio; Gubbiotti, Stefania. - In: BIOMETRICAL JOURNAL. - ISSN 0323-3847. - 64:4(2022), pp. 681-695. [10.1002/bimj.202100035]

Joint control of consensus and evidence in Bayesian design of clinical trials

De Santis, Fulvio;Gubbiotti, Stefania
2022

Abstract

In Bayesian inference, prior distributions formalize preexperimental information and uncertainty on model parameters. Sometimes different sources of knowledge are available, possibly leading to divergent posterior distributions and inferences. Research has been recently devoted to the development of sample size criteria that guarantee agreement of posterior information in terms of credible intervals when multiple priors are available. In these articles, the goals of reaching consensus and evidence are typically kept separated. Adopting a Bayesian performance-based approach, the present article proposes new sample size criteria for superiority trials that jointly control the achievement of both minimal evidence and consensus, measured by appropriate functions of the posterior distributions. We develop both an average criterion and a more stringent criterion that accounts for the entire predictive distributions of the selected measures of minimal evidence and consensus. Methods are developed and illustrated via simulation for trials involving binary outcomes. A real clinical trial example on Covid-19 vaccine data is presented.
2022
credible intervals; pre-posterior analysis; sample size determination; superiority trials
01 Pubblicazione su rivista::01a Articolo in rivista
Joint control of consensus and evidence in Bayesian design of clinical trials / De Santis, Fulvio; Gubbiotti, Stefania. - In: BIOMETRICAL JOURNAL. - ISSN 0323-3847. - 64:4(2022), pp. 681-695. [10.1002/bimj.202100035]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1600955
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