Greenland S, Poole C
Department of Epidemiology, UCLA School of Public Health.
Arch Environ Health. 1994 Jan-Feb;49(1):9-16. doi: 10.1080/00039896.1994.9934409.
Empirical-Bayes methods offer potentially dramatic improvements in statistical accuracy over conventional statistical methods. We provide an elementary introduction to empirical-Bayes analysis of occupational and environmental hazard surveillance data. Such analyses are especially well suited to situations in which many associations must be examined, but few or none can be estimated precisely. Statistical issues in hazard surveillance are reviewed, followed by a discussion of the rationale and methods for empirical-Bayes analyses, using a study of occupational exposures and cancer mortality to illustrate key concepts. Finally, the assumptions underlying empirical-Bayes analyses are discussed critically, with special attention to the "exchangeability" assumptions that distinguish empirical-Bayes from conventional methods.
经验贝叶斯方法相较于传统统计方法在统计准确性方面可能有显著提升。我们对职业和环境危害监测数据的经验贝叶斯分析进行初步介绍。此类分析特别适用于必须检查许多关联，但几乎没有或根本无法精确估计的情况。我们先回顾危害监测中的统计问题，接着讨论经验贝叶斯分析的基本原理和方法，并通过一项职业暴露与癌症死亡率的研究来说明关键概念。最后，对经验贝叶斯分析所依据的假设进行批判性讨论，特别关注将经验贝叶斯与传统方法区分开来的“可交换性”假设。
