Deciding whether follow-up studies have replicated findings in a preliminary large-scale "omics' study"

Abstract

We propose a formal method to declare that findings from a primary study have been replicated in a follow-up study. Our proposal is appropriate for primary studies that involve large-scale searches for rare true positives (i.e. needles in a haystack). Our proposal assigns an $r$-value to each finding; this is the lowest false discovery rate at which the finding can be called replicated. Examples are given and software is available.

References in corpus (5)

• Measuring reproducibility of high-throughput experiments
• Replication in Genome-Wide Association Studies
• Replicability analysis for genome-wide association studies
• Discovering findings that replicate from a primary study of high dimension to a follow-up study
• Statistical tests for the intersection of independent lists of genes: Sensitivity, FDR, and type I error control

Cited by in corpus (6)

• Assessing replicability of findings across two studies of multiple features
• Extracting replicable associations across multiple studies: algorithms for controlling the false discovery rate
• Assessing replicability with the sceptical p-value: Type-I error control and sample size planning
• The replication of equivalence studies
• Randomized p-values for multiple testing and their application in replicability analysis
• Statistical Assessment of Replicability via Bayesian Model Criticism