Strug Lisa J
Program in Genetics and Genome Biology, The Hospital for Sick Children, The Centre for Applied Genomics, The Hospital for Sick Children, Division of Biostatistics and Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada.
Genet Epidemiol. 2018 Oct;42(7):590-607. doi: 10.1002/gepi.22151. Epub 2018 Aug 18.
Concerns over reproducibility in research has reinvigorated the discourse on P-values as measures of statistical evidence. In a position statement by the American Statistical Association board of directors, they warn of P-value misuse and refer to the availability of alternatives. Despite the common practice of comparing P-values across different hypothesis tests in genetics, it is well-appreciated that P-values must be interpreted alongside the sample size and experimental design used for their computation. Here, we discuss the evidential statistical paradigm (EP), an alternative to Bayesian and Frequentist paradigms, that has been implemented in human genetics studies. Using applications in Cystic Fibrosis genetic association analyses, and describing recent theoretical developments, we review how to measure statistical evidence using the EP in the presence of covariates, model misspecification, and for composite hypotheses. Novel graphical displays are presented, and software for their computation is highlighted. The implications of multiple hypothesis testing for the EP are delineated in the analyses, demonstrating a view more consistent with scientific reasoning; the EP provides a theoretical justification for replication that is a requirement in genetic association studies. As genetic studies grow in size and complexity, a fresh look at measures of statistical evidence that are sensible amid the analysis of big data are required.
对研究可重复性的担忧重新激发了关于将P值作为统计证据度量的讨论。在美国统计协会董事会的一份立场声明中，他们警告了P值的滥用，并提及了替代方法的可用性。尽管在遗传学中比较不同假设检验的P值是常见做法，但人们普遍认识到，P值必须结合用于计算的样本量和实验设计来解释。在此，我们讨论证据统计范式（EP），它是贝叶斯范式和频率主义范式的替代方法，已在人类遗传学研究中得到应用。通过囊性纤维化基因关联分析中的应用，并描述近期的理论发展，我们回顾了在存在协变量、模型错误设定以及复合假设的情况下，如何使用EP来度量统计证据。展示了新颖的图形显示，并强调了用于其计算的软件。分析中阐述了多重假设检验对EP的影响，展示了一种与科学推理更一致的观点；EP为遗传关联研究中所需的重复提供了理论依据。随着基因研究规模和复杂性的增加，需要重新审视在大数据分析中合理的统计证据度量方法。
