檢定多重擾動以偵測微弱相關

Abstract

在流行病學和生物醫學研究中，許多危險因子或介入的效應非常微小。為了偵測這些微小的效應，研究必須要具有大樣本數，也就是研究個案要夠多。研究者當然可以增加樣本數，但是有其限制。在這篇論文中，我們提出一個嶄新的方法，以不同方向來增加樣本數，也就是增加變項數量(p)。我們建構一個以p為本的「多重擾動檢定」，並且進行理論統計檢力計算和電腦模擬。當p非常大時，如數千甚至數百萬，多重擾動檢定可以達到很高的統計檢力來偵測微弱的效應。我們還應用多重擾動檢定來重新分析一個已經發表的老年性黃斑部病變的全基因體相關性研究。我們找出兩個和疾病相關而且新的顯著基因。這個以p為本的多重擾動檢定，相信在未來，可以樹立一個新的統計上假設檢定的典範。

Many risk factors/interventions in epidemiologic/biomedical studies are of minuscule effects. To detect such weak associations, one needs a study with a very large sample size (the number of subjects, n). The n of a study can of course be increased but only to an extent. In this paper, the authors propose a novel method which hinges on increasing sample size in a different direction—the total number of variables (p). The authors construct a p-based ‘multiple perturbation test’, and conduct theoretical power calculations and computer simulations to show that it can achieve a very high power to detect weak associations when p can be made very large, say, to the thousands or millions. The authors apply the method to re-analyze a published genome-wide association study on age-related macular degeneration and identify two novel genetic variants that are significantly associated with the disease. The p-based method may set a stage for a new paradigm of statistical hypothesis tests.