Work

Genomics, Prior Probability, and Statistical Tests of Multiple Hypotheses

Year: 2004

Type: preprint

Abstract: Genomic methods have made statistical multiple-test methods important to geneticists and molecular biologists. These tests apply to identification of quantitative trait loci and measurement of changes in RNA or DNA abundance by microarray methods. Recently developed multiple-test methods provide more statistical power when many of the tested null hypotheses are false. At the same time, these methods can provide stringent control of errors in cases when most or all of the tested null hypotheses are true. These methods control errors in a different way from previous hypothesis tests, controlling or estimating quantities called the posterior error rate (PER), false discovery rate (FDR), or proportion of false positives (PFP), rather than the type I error. In this study, we attempt to clarify the relationships among these methods and demonstrate how the proportion of true null hypotheses among all tested hypotheses plays an important role.

Source: Genome Research

DOI: 10.1101/gr.2156804

Authors: Kenneth F. Manly, Dan Nettleton, J.T. Gene Hwang

Institutions: Roswell Park Comprehensive Cancer Center, Iowa State University, Cornell University

Language: English

Topic: Statistical Methods in Clinical Trials

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: bronze