With the introduction of high-throughput technologies in clinical and epidemiological studies, the need for inferential tools that are able to deal with fat data-structures, i.e., relatively small number of observations compared to the number of features, is becoming more prominent. To solve this problem, we propose an extension of the dgLARS method to relative risk regression model. The main idea of proposed method is to use the differential geometric structure of the partial likelihood function in order to select the optimal subset of covariates.