Joint control of consensus and evidence in Bayesian design of clinical trials.
Biom J
; 64(4): 681-695, 2022 04.
Article
in English
| MEDLINE | ID: covidwho-1565174
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19 Vaccines
/
COVID-19
Type of study:
Experimental Studies
/
Prognostic study
/
Randomized controlled trials
Topics:
Vaccines
Limits:
Humans
Language:
English
Journal:
Biom J
Year:
2022
Document Type:
Article
Affiliation country:
Bimj.202100035
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