A multiple testing procedure to associate gene expression levels with survival

In many microarray studies the primary objective is to identify, from a large panel of genes, those which are prognostic markers of a censored survival endpoint such as time to disease recurrence or death. Often, these genes are considered prognostic in that their respective expressions are associated with the survival endpoint of interest. To assess this association requires specifying an appropriate measure of association, a suitable test statistic and, as the number of genes is large, proper handling of multiplicity issues. In this paper, we will address these issues by utilizing a general correlation measure, a non-parametric test statistic, and control of the family-wise error rate by employing permutation resampling. Comprehensive simulation studies are conducted to investigate the statistical properties of the proposed procedure. The proposed method is applied to a recently published data set on patients with lung cancer.