贝叶斯Meta-分析
发布时间：2018-09-03 15:13
【摘要】：Meta-分析方法是一种对具有相同研究目的的多个研究结果进行合并,并综合评价结果的统计学方法,该方法在循证医学领域有着广泛的应用.当某事件概率发生很小时,Meta-分析经常会遇到稀疏数据问题,针对稀疏数据的处理目前主要有两种方法:一是对其进行连续性修正;二是利用贝叶斯方法进行分析.本文主要是基于贝叶斯方法进行Meta-分析,给出了 Jeffreys无信息先验Beta(1/2,1/2)时的链接分布函数:并将其推广,给出了先验分布为一般贝塔分布Beta(α,β)时的链接分布函数:最后我们给出了数值模拟结果,并讨论了在可选择的范围内t值的变化对试验效果的影响.
[Abstract]:Meta- analysis is a statistical method that combines and synthetically evaluates the results of multiple studies with the same purpose. This method is widely used in the field of evidence-based medicine. The problem of sparse data is often encountered when the probability of an event is very small. There are two main methods to deal with sparse data: one is to modify it continuously; the other is to use Bayesian method to analyze it. In this paper, the Meta- analysis based on Bayesian method is used, and the link distribution function of Jeffreys Beta (1 / 2 / 1 / 2) is given. The link distribution function when the prior distribution is a general Beta distribution Beta (伪, 尾) is given. Finally, the numerical simulation results are given, and the influence of the change of t value on the test results is discussed.
【学位授予单位】：山东师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：C81
本文编号：2220351
[Abstract]:Meta- analysis is a statistical method that combines and synthetically evaluates the results of multiple studies with the same purpose. This method is widely used in the field of evidence-based medicine. The problem of sparse data is often encountered when the probability of an event is very small. There are two main methods to deal with sparse data: one is to modify it continuously; the other is to use Bayesian method to analyze it. In this paper, the Meta- analysis based on Bayesian method is used, and the link distribution function of Jeffreys Beta (1 / 2 / 1 / 2) is given. The link distribution function when the prior distribution is a general Beta distribution Beta (伪, 尾) is given. Finally, the numerical simulation results are given, and the influence of the change of t value on the test results is discussed.
【学位授予单位】：山东师范大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：C81
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相关期刊论文 前3条
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