Objective Bayesian meta-analysis for sparse discrete data.
This paper presents a Bayesian model for meta-analysis of sparse discrete binomial data, which are out of the scope of the usual hierarchical normal random-effect models. Treatment effectiveness data are often of this type. The crucial linking distribution between the effectiveness conditional on the healthcare center and the unconditional effectiveness is constructed from specific bivariate classes of distributions with given marginals. This assures coherency between the marginal and conditional prior distributions utilized in the analysis. Further, we impose a bivariate class of priors that is able to accommodate a wide range of heterogeneity degrees between the multicenter clinical trials involved. Applications to real multicenter data are given and compared with previous meta-analysis.
Department of Statistics, University of Granada, Granada, E-18071, Spain.
E
E
Moreno
F J
FJ
Vázquez-Polo
M A
MA
Negrín
D001499:Bayes Theorem; D002318:Cardiovascular Diseases / Q000209:etiology; D003924:Diabetes Mellitus, Type 2 / Q000188:drug therapy / Q000209:etiology; D016640:Diabetes, Gestational; D004827:Epilepsy / Q000188:drug therapy; D005260:Female; D020249:Hormone Replacement Therapy; D006801:Humans; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D015337:Multicenter Studies as Topic; D011247:Pregnancy; D000077154:Rosiglitazone; D045162:Thiazolidinediones / Q000633:toxicity; D016896:Treatment Outcome; D005680:gamma-Aminobutyric Acid / Q000031:analogs & derivatives / Q000627:therapeutic use
D045162:Thiazolidinediones; D000077154:Rosiglitazone; C017985:progabide; D005680:gamma-Aminobutyric Acid
Farlie-Gumbel-Morgenstern link distribution; Fréchet class; Sarmanov link distribution; intrinsic link distribution; meta-predictive distribution; testing on meta-parameters