Copula-Based Estimation Methods for a Common Mean Vector for Bivariate Meta-Analyses

Symmetry

Takeshi Emura¹  Yoshihiko Konno²  Yuan-Tsung Chang³  Jia-Han Shih⁴ 

[1] Biostatistics Center, Kurume University, Kurume, Fukuoka 830-0011, Japan;Department of Mathematical and Physical Sciences, Japan Women’s University, Tokyo 112-8681, Japan;Department of Social Information, Mejiro University, Tokyo 161-8539, Japan;Institute of Statistical Science, Academia Sinica, Taipei 11529, Taiwan;

关键词: bivariate distribution;    copula;    correlation;    FGM copula;    maximum likelihood estimator;    meta-analysis;   

DOI  :  10.3390/sym14020186

来源: DOAJ

【 摘 要 】

Traditional bivariate meta-analyses adopt the bivariate normal model. As the bivariate normal distribution produces symmetric dependence, it is not flexible enough to describe the true dependence structure of real meta-analyses. As an alternative to the bivariate normal model, recent papers have adopted “copula” models for bivariate meta-analyses. Copulas consist of both symmetric copulas (e.g., the normal copula) and asymmetric copulas (e.g., the Clayton copula). While copula models are promising, there are only a few studies on copula-based bivariate meta-analysis. Therefore, the goal of this article is to fully develop the methodologies and theories of the copula-based bivariate meta-analysis, specifically for estimating the common mean vector. This work is regarded as a generalization of our previous methodological/theoretical studies under the FGM copula to a broad class of copulas. In addition, we develop a new R package, “CommonMean.Copula”, to implement the proposed methods. Simulations are performed to check the proposed methods. Two real dataset are analyzed for illustration, demonstrating the insufficiency of the bivariate normal model.

【 授权许可】

Unknown