Bayesian versus frequentist statistical inference for investigating a one-off cancer cluster reported to a health department.
BMC Med Res Methodol
; 9: 30, 2009 May 11.
Article
em En
| MEDLINE
| ID: mdl-19426561
ABSTRACT
BACKGROUND:
The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods.METHODS:
This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution.RESULTS:
Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective whether there is an apparent statistical excess depends on the specified prior.CONCLUSION:
In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Saúde Pública
/
Teorema de Bayes
/
Biometria
/
Neoplasias
Tipo de estudo:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Limite:
Adolescent
/
Adult
/
Female
/
Humans
/
Middle aged
País/Região como assunto:
Oceania
Idioma:
En
Ano de publicação:
2009
Tipo de documento:
Article