A Bayesian meta-analysis of prophylactic granulocyte colony-stimulating factor and granulocyte-macrophage colony-stimulating factor in children with cancer

L. Sung, J. Beyene, J. Hayden, P. C. Nathan, B. Lange, G. A. Tomlinson

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

14 Citas (Scopus)

Resumen

The purpose of this analysis was to examine the efficacy of prophylactic hematopoietic colony-stimulating factors (CSFs) in pediatric cancer and to describe how a Bayesian meta-analysis can be conducted and then modified to incorporate information not readily included in a frequentist meta-analysis. Three Bayesian models were developed. The simplest model used the same data as a published frequentist meta-analysis. The second model included data that could not easily be incorporated into the frequentist meta-analysis, including data from different courses of chemotherapy and continuous outcomes that did not report variance estimates. The third model examined the effect of CSF type (granulocyte CSF vs. granulocyte-macrophage CSF). Compared with the frequentist model, the Bayesian model with the most data suggested a greater benefit of CSFs, with a 3.2-day reduction in duration of parenteral antibiotics (95% credible interval: -7.1, 0.7) in the expanded Bayesian model compared with a 0.8-day (95% confidence interval: -2.3, 0.7) reduction in the frequentist model. Bayesian meta-analysis also suggested that, compared with granulocyte-macrophage CSF, granulocyte CSF was associated with a 4.8-day decrease in the duration of parenteral antibiotics. Bayesian meta-analysis can readily include information not easily incorporated in a frequentist meta-analysis. Some treatment effect estimates were larger by a clinically important amount when additional data contributed to the pooled estimate.

Idioma originalEnglish
Páginas (desde-hasta)811-817
Número de páginas7
PublicaciónAmerican Journal of Epidemiology
Volumen163
N.º9
DOI
EstadoPublished - 2006
Publicado de forma externa

ASJC Scopus Subject Areas

  • Epidemiology

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