ENHANCING POWER OF SCORE TESTS FOR REGRESSION MODELS VIA FISHER TRANSFORMATION

抄録

A simple method is presented to enhance statistical power of score tests for regression models via Fisher transformation (or Fisher’s z-transformation) by exploiting a relationship with the partial correlation coefficient. Simulation studies mimicking marginal association and gene-environment interaction analyses for genome-wide association studies (GWASs) under case-control design demonstrate that the Fisher transformation enhances power of the score tests while maintaining type I error asymptotically. The smaller the sample size is, the more the enhancement is pronounced, at the expense of inflated type I error due to invalidating asymptotic approximation. Accordingly, the proposed method may be applied when sample size is enough for valid asymptotic approximation. An illustration with real GWAS data is also presented.