Can matching improve the performance of boosting for identifying important genes in observational studies?

Abstract

When two groups of individuals are to be compared with respect to gene expression there will often be some potentially confounding variables that differ between the groups. Matching is an established approach for obtaining comparable groups and enabling subsequent univariate tests for each gene. Alternatively, the confounders might be incorporated directly into a multivariable regression model for adjustment. In contrast to univariate tests, such models can consider all genes simultaneously. Aiming to combine the advantages of both approaches, matching and multivariable modeling, we consider a matching-based boosting procedure for fitting risk prediction models in two-group settings. This possibly allows to identify and automatically remove problematic observations that might negatively affect the regression model. Therefore, we compare the ability to identify important covariates for this combination of matching and boosting with only boosting for different covariate correlation structures in a simulation study. Furthermore, we analyze the prediction performance of these approaches on two gene expression microarray studies. The first study comprises patients with B-cell and T-cell type acute lymphoblastic leukemia and the second patients with acute megakaryoblastic leukemia. While the matching component can in principle guard against problematic observations, the combined approach is seen to neither improve identification of important covariates nor to improve prediction performance. Therefore, a combination of the two approaches cannot be recommended. Adjustment for potential confounders is seen to provide the best performance, i.e. a pure multivariable regression modeling strategy seems to be promising even in presence of considerable heterogeneity.