Statistical aspects of omics data analysis using the random compound covariate

Dealing with high dimensional markers, such as gene expression data obtained using microarray chip technology or genomics studies, is a key challenge because the numbers of features greatly exceeds the number of biological samples. After selecting biologically relevant genes, how to summarize the expression of selected genes and then further build predicted model is an important issue in medical applications. One intuitive method of addressing this challenge assigns different weights to different features, subsequently combining this information into a single score, named the compound covariate. Investigators commonly employ this score to assess whether an association exists between the compound covariate and clinical outcomes adjusted for baseline covariates. However, we found that some clinical papers concerned with such analysis report bias p-values based on flawed compound covariate in their training data set.

We correct this flaw in the analysis and we also propose treating the compound score as a random covariate, to achieve more appropriate results and significantly improve study power for survival outcomes. With this proposed method, we thoroughly assess the performance of two commonly used estimated gene weights through simulation studies. When the sample size is 100, and censoring rates are 50%, 30%, and 10%, power is increased by 10.6%, 3.5%, and 0.4%, respectively, by treating the compound score as a random covariate rather than a fixed covariate. Finally, we assess our proposed method using two publicly available microarray data sets.

In this article, we correct this flaw in the analysis and the propose method, treating the compound score as a random covariate, can achieve more appropriate results and improve study power for survival outcomes.