Sensitivity and identification quantification by a relative latent model complexity perturbation in Bayesian meta-analysis.

ABSTRACT: In recent years, Bayesian meta-analysis expressed by a normal-normal hierarchical model (NNHM) has been widely used for combining evidence from multiple studies. Data provided for the NNHM are frequently based on a small number of studies and on uncertain within-study standard deviation values. Despite the widespread use of Bayesian NNHM, it has always been unclear to what extent the posterior inference is impacted by the heterogeneity prior (sensitivity S ) and by the uncertainty in the within-study standard deviation values (identification I ). Thus, to answer this question, we developed a unified method to simultaneously quantify both sensitivity and identification ( S - I ) for all model parameters in a Bayesian NNHM, based on derivatives of the Bhattacharyya coefficient with respect t

SUBMITTER: Roos M 

PROVIDER: S-EPMC9292837 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature