Comparing LASSO and IPF-LASSO for multi-modal data: variable selection with Type I error control

Variable selection in high-dimensional regression models is challenging. Thus, developing stable and reliable methods for variable selection is essential. Omics data, a common source of high-dimensional data, brings the added complexity of integrating diverse genomic layers into the analysis. The IPF-LASSO model has previously addressed this by applying distinct penalty parameters for each data modality. However, incorporating heterogeneous data layers into variable selection with Type I error control remains an open problem. To address this, we applied stability selection to control the number of false positives in both IPF-LASSO and standard LASSO models. Our study aimed to compare the two methods, investigating whether introducing different penalty parameters per data modality enhances statistical power while controlling false positives. Two high-dimensional data structures were investigated in simulations, one with independent data and the other with correlated data. We also applied the models to breast cancer treatment data, where IPF-LASSO identified relevant clinical variables.