The integration of high-dimensional genomic data and clinical data into time-to-event prediction models has gained significant attention due to the growing availability of these datasets. Traditionally, a Cox regression model is employed, concatenating various covariate types log-linearly. Given that much of the data may be redundant or irrelevant, feature selection through penalization is often desirable. A notable characteristic of these datasets is their organization into blocks of distinct data types, such as methylation and clinical predictors, which require selecting a subset of covariates from each group due to high intra-group correlations. However, existing grouped variable selection methods do not ensure that variables are chosen from every group. As a result, smaller groups such as clinical predictors that are crucial for survival prediction may be under-represented. For this reason, we propose utilizing Exclusive Lasso regularization instead of standard Lasso penalization. Exclusive Lasso combines an L 1 -norm penalty, which induces sparsity within each group, with an L 2 -norm penalty, which promotes balanced selection across groups, ensuring that at least one variable is retained from each. To illustrate the advantages of this approach, we apply it to simulated datasets and real-world cancer data, assessing its applicability and comparing its survival prediction and variable selection performance with that of the conventional Cox regression model and other state-of-the-art methods.
Ravi et al. (Thu,) studied this question.