Hunting for Significance With the False Discovery Rate

• 成果类型: Article
• 署名作者: Posch, Martin; Zehetmayer, Sonja; Bauer, Peter
• 署名单位: Medical University of Vienna
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.0137
• 发表日期: 2009
• 页码: 832-840
• 关键词: gene-expression; 2-stage designs; PROPORTION

摘要

When testing a single hypothesis. it is common knowledge that increasing the sample size after nonsignificant results and repeating the hypothesis test several times at unadjusted critical levels inflates tire overall Type I error rate severely. In contrast, if a large number of hypotheses are tested controlling the False Discovery Rate, such hunting for significance has asymptotically no impact on the error rate. More specifically. if the sample size is increased for all hypotheses simultaneously and only tire test at the final interim analysis determines which hypotheses are rejected, a data dependent increase or sample size does riot affect tire False Discovery Rate. This holds asymptotically (for an increasing number of hypotheses) for all scenarios but the global null hypothesis where all hypotheses are true. To control the False Discovery Rate also under the global null hypothesis, we consider stopping rules where stopping before a predefined maximum sample size is reached is possible only if sufficiently many null hypotheses can he rejected. The procedure is illustrated with several datasets from microarray experiments.