Testing for publication bias in diagnostic meta-analysis: a simulation study.
Stat Med
; 33(18): 3061-77, 2014 Aug 15.
Article
em En
| MEDLINE
| ID: mdl-24753050
ABSTRACT
The present study investigates the performance of several statistical tests to detect publication bias in diagnostic meta-analysis by means of simulation. While bivariate models should be used to pool data from primary studies in diagnostic meta-analysis, univariate measures of diagnostic accuracy are preferable for the purpose of detecting publication bias. In contrast to earlier research, which focused solely on the diagnostic odds ratio or its logarithm ( ln ω), the tests are combined with four different univariate measures of diagnostic accuracy. For each combination of test and univariate measure, both type I error rate and statistical power are examined under diverse conditions. The results indicate that tests based on linear regression or rank correlation cannot be recommended in diagnostic meta-analysis, because type I error rates are either inflated or power is too low, irrespective of the applied univariate measure. In contrast, the combination of trim and fill and ln ω has non-inflated or only slightly inflated type I error rates and medium to high power, even under extreme circumstances (at least when the number of studies per meta-analysis is large enough). Therefore, we recommend the application of trim and fill combined with ln ω to detect funnel plot asymmetry in diagnostic meta-analysis.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Metanálise como Assunto
/
Viés de Publicação
/
Diagnóstico
Tipo de estudo:
Diagnostic_studies
/
Etiology_studies
/
Risk_factors_studies
/
Systematic_reviews
Limite:
Humans
Idioma:
En
Ano de publicação:
2014
Tipo de documento:
Article