Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Medical decision making, 2014-10, Vol.34 (7), p.911-918
2014

Details

Autor(en) / Beteiligte
Titel
Use of Generalized Linear Mixed Models for Network Meta-analysis
Ist Teil von
  • Medical decision making, 2014-10, Vol.34 (7), p.911-918
Ort / Verlag
Los Angeles, CA: SAGE Publications
Erscheinungsjahr
2014
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • In the past decade, a new statistical method—network meta-analysis—has been developed to address limitations in traditional pairwise meta-analysis. Network meta-analysis incorporates all available evidence into a general statistical framework for comparisons of multiple treatments. Bayesian network meta-analysis, as proposed by Lu and Ades, also known as “mixed treatments comparisons,” provides a flexible modeling framework to take into account complexity in the data structure. This article shows how to implement the Lu and Ades model in the frequentist generalized linear mixed model. Two examples are provided to demonstrate how centering the covariates for random effects estimation within each trial can yield correct estimation of random effects. Moreover, under the correct specification for random effects estimation, the dummy coding and contrast basic parameter coding schemes will yield the same results. It is straightforward to incorporate covariates, such as moderators and confounders, into the generalized linear mixed model to conduct meta-regression for multiple treatment comparisons. Moreover, this approach may be extended easily to other types of outcome variables, such as continuous, counts, and multinomial.

Weiterführende Literatur

Empfehlungen zum selben Thema automatisch vorgeschlagen von bX