Rare-Screening Technique for Binary Outcome with Rare Events

Music City Center 

Epigenome-wide association studies often involve large-scale DNA methylation data. Efficient screening of CpG sites associated with binary outcomes, especially when the events are rare (<5%), is challenging. Existing screening methods, such as ttScreening, effectively filter out unimportant variables (e.g., CpG sites) in epigenome-wide studies. However, they do not work well for binary outcomes with rare events. To address this, we developed a novel screening method that combines resampling with replacement and empirical Bayes adjustment to stabilize estimates and improve sensitivity. In parallel, we implemented a ttScreening approach embedding logistic regression with Firth's penalty term to mitigate bias in rare event contexts. We evaluate the performance of the proposed approaches and benchmark methods, using FDR and Bonferroni adjustments for multiple testing, through extensive simulations with varying sample sizes and number of parameters. The results show that the proposed methods have a higher sensitivity compared to the benchmark methods. To facilitate implementation, we have developed an R package, rareScreening, now available on GitHub.

Screening

rare-event

sensitivity

resampling 

Section on Statistics in Genomics and Genetics