Publication bias and meta‐analysis for 2×2 tables: an average Markov chain Monte Carlo EM algorithm
Publication bias and meta‐analysis for 2×2 tables: an average Markov chain Monte Carlo EM algorithm
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2×2 表的发表偏差和荟萃分析：平均马尔可夫链蒙特卡罗 EM 算法
DOI：
10.1111/1467-9868.00334
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
J. Copas
中科院分区：
文献类型：
--
作者：
J. Shi;J. Copas
概括。荟萃分析的一个主要困难是发表偏倚。具有积极结果的研究比报告消极或不确定结果的研究更有可能发表。如果不做出无法检验的假设，就不可能纠正这种偏差。在本文中，讨论了使用精确条件分布对 2×2 表进行荟萃分析的敏感性分析。马尔可夫链蒙特卡罗 EM 算法用于计算最大似然估计。建议增加估计的准确性并自动选择迭代次数的规则。
Summary. A major difficulty in meta‐analysis is publication bias. Studies with positive outcomes are more likely to be published than studies reporting negative or inconclusive results. Correcting for this bias is not possible without making untestable assumptions. In this paper, a sensitivity analysis is discussed for the meta‐analysis of 2×2 tables using exact conditional distributions. A Markov chain Monte Carlo EM algorithm is used to calculate maximum likelihood estimates. A rule for increasing the accuracy of estimation and automating the choice of the number of iterations is suggested.