Bayesian consensus-based sample size criteria for binomial proportions.
Stat Med
; 38(23): 4566-4573, 2019 10 15.
Article
en En
| MEDLINE
| ID: mdl-31297825
Many sample size criteria exist. These include power calculations and methods based on confidence interval widths from a frequentist viewpoint, and Bayesian methods based on credible interval widths or decision theory. Bayesian methods account for the inherent uncertainty of inputs to sample size calculations through the use of prior information rather than the point estimates typically used by frequentist methods. However, the choice of prior density can be problematic because there will almost always be different appreciations of the past evidence. Such differences can be accommodated a priori by robust methods for Bayesian design, for example, using mixtures or ϵ-contaminated priors. This would then ensure that the prior class includes divergent opinions. However, one may prefer to report several posterior densities arising from a "community of priors," which cover the range of plausible prior densities, rather than forming a single class of priors. To date, however, there are no corresponding sample size methods that specifically account for a community of prior densities in the sense of ensuring a large-enough sample size for the data to sufficiently overwhelm the priors to ensure consensus across widely divergent prior views. In this paper, we develop methods that account for the variability in prior opinions by providing the sample size required to induce posterior agreement to a prespecified degree. Prototypic examples to one- and two-sample binomial outcomes are included. We compare sample sizes from criteria that consider a family of priors to those that would result from previous interval-based Bayesian criteria.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Ensayos Clínicos como Asunto
/
Teorema de Bayes
/
Tamaño de la Muestra
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Stat Med
Año:
2019
Tipo del documento:
Article
País de afiliación:
Canadá
Pais de publicación:
Reino Unido