Wakefield Jon
Department of Statistics, University of Washington, Box 357232, Seattle, Washington 98195-7232, USA.
Genet Epidemiol. 2009 Jan;33(1):79-86. doi: 10.1002/gepi.20359.
The Bayes factor is a summary measure that provides an alternative to the P-value for the ranking of associations, or the flagging of associations as "significant". We describe an approximate Bayes factor that is straightforward to use and is appropriate when sample sizes are large. We consider various choices of the prior on the effect size, including those that allow effect size to vary with the minor allele frequency (MAF) of the marker. An important contribution is the description of a specific prior that gives identical rankings between Bayes factors and P-values, providing a link between the two approaches, and allowing the implications of the use of P-values to be more easily understood. As a summary measure of noteworthiness P-values are difficult to calibrate since their interpretation depends on MAF and, crucially, on sample size. A consequence is that a consistent decision-making procedure using P-values requires a threshold for significance that reduces with sample size, contrary to common practice.
贝叶斯因子是一种汇总度量，它为关联的排序或作为“显著”关联的标记提供了一种替代P值的方法。我们描述了一种近似贝叶斯因子，它易于使用，并且在样本量较大时适用。我们考虑了效应大小先验的各种选择，包括那些允许效应大小随标记的次要等位基因频率（MAF）变化的选择。一个重要的贡献是描述了一种特定的先验，它在贝叶斯因子和P值之间给出相同的排序，在两种方法之间建立了联系，并使P值使用的含义更容易理解。作为值得注意程度的汇总度量，P值难以校准，因为它们的解释取决于MAF，并且至关重要的是取决于样本量。结果是，使用P值的一致决策程序需要一个显著性阈值，该阈值随样本量减小，这与常见做法相反。
