Pounds Stan, Cheng Cheng
Department of Biostatistics, St. Jude Children's Research Hospital 332 N. Lauderdale Street, Memphis, TN 38135, USA.
Bioinformatics. 2006 Aug 15;22(16):1979-87. doi: 10.1093/bioinformatics/btl328. Epub 2006 Jun 15.
Presently available methods that use p-values to estimate or control the false discovery rate (FDR) implicitly assume that p-values are continuously distributed and based on two-sided tests. Therefore, it is difficult to reliably estimate the FDR when p-values are discrete or based on one-sided tests.
A simple and robust method to estimate the FDR is proposed. The proposed method does not rely on implicit assumptions that tests are two-sided or yield continuously distributed p-values. The proposed method is proven to be conservative and have desirable large-sample properties. In addition, the proposed method was among the best performers across a series of 'real data simulations' comparing the performance of five currently available methods.
Libraries of S-plus and R routines to implement the method are freely available from www.stjuderesearch.org/depts/biostats.
目前可用的使用p值来估计或控制错误发现率(FDR)的方法隐含地假设p值是连续分布的且基于双侧检验。因此,当p值是离散的或基于单侧检验时,很难可靠地估计FDR。
提出了一种简单且稳健的估计FDR的方法。所提出的方法不依赖于检验是双侧的或产生连续分布的p值的隐含假设。所提出的方法被证明是保守的并且具有理想的大样本性质。此外,在一系列比较五种当前可用方法性能的“真实数据模拟”中,所提出的方法是表现最佳的方法之一。
可从www.stjuderesearch.org/depts/biostats免费获得用于实现该方法的S-plus和R程序库。