Abstract: Objective To introduce a model of meta-analysis, and to explain how to fit the model with bayesian Methods.Methods The normal-normal hierarchical model(NNHM)was explained. Random effects model was used to reanalyze two worked examples from literature in the framework of the NNHM. Bayesian Methods were used to fit the NNHM with the bayesmeta R package. The priors for the effect parameter μ and heterogeneity parameter τ were given different choice respectively.Results In the framework of NNHM, based on the different priors, the Results of data 1 were shown that point estimates and 95% credit interval(CI) of parameter μ using the bayesian method were -4.26(-6.97, -1.92) and -4.50(-9.27, -0.53) respectively and point estimates and 95%CI of parameter τ were 1.51(0.41, 2.75) and 2.28(0.00, 6.57) respectively. And the Results of data 2 were shown that point estimates and 95%CI of parameter μ were -4.07(-5.54, -2.71) and -4.12(-5.96, -2.46) respectively and point estimates and 95% CI of parameter τ were 1.54(0.78, 2.48) and 1.81(0.74, 3.51) respectively.Conclusion Different priors might have an effect on the estimation of parameters. Bayesian Methods were preferable for the meta-analyses of continuous data in single-arm trials within the framework of NNHM, and the bayesmeta package was allowed to perform bayesian random-effects meta-analyses because of its easy, quick, accurate and reproducible computation.

Key words: Bayesian methods, Bayesmeta package, Continuous data, Meta-analysis, Normal-normal hierarchical model, Single-arm trials