DART2在辅助信息好坏未知时仍能稳定控制错误发现率并提升检验功效。
DART2: a robust multiple testing method to smartly leverage helpful or misleading ancillary information
- 基于距离的检验方法，自动适应辅助信息的质量。
- 无论辅助信息是否有效，都能保证错误发现率控制且功效不下降。
- 适用于基因关联研究等需依赖外部信息的场景。
在多重假设检验中，常有反映原假设或备择假设状态的辅助信息可用。现有方法通常要求辅助信息足够有效才能提升检验功效，但其性能随信息质量波动。本文提出一种稳健有效的距离辅助多重检验方法 DART2，无论辅助信息是有益还是误导性的，均能保持高功效并严格控制错误发现率（FDR）。当辅助信息有益时，DART2 可渐近控制 FDR 并提升检验功效；当辅助信息无效甚至误导时，仍可保证 FDR 控制，且功效不低于忽略辅助信息的情况。通过多种数值实验验证了 DART2 相较于现有方法的优越性。此外，该方法应用于基因关联研究，在两种不同类型的辅助信息下均表现出更优的准确性和鲁棒性。
原文摘要 · Abstract (English)
In many applications of multiple testing, ancillary information is available, reflecting the hypothesis null or alternative status. Several methods have been developed to leverage this ancillary information to enhance testing power, typically requiring the ancillary information is helpful enough to ensure favorable performance. In this paper, we develop a robust and effective distance-assisted multiple testing procedure named DART2, designed to be powerful and robust regardless of the quality of ancillary information. When the ancillary information is helpful, DART2 can asymptotically control FDR while improving power; otherwise, DART2 can still control FDR and maintain power at least as high as ignoring the ancillary information. We demonstrated DART2's superior performance compared to existing methods through numerical studies under various settings. In addition, DART2 has been applied to a gene association study where we have shown its superior accuracy and robustness under two different types of ancillary information.
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