Structural modeling using overlapped group penalties for discovering predictive biomarkers for subgroup analysis
This work addresses the challenge of discovering predictive biomarkers for subgroup analysis in medical research, representing an incremental improvement with a novel penalty function and optimization algorithm.
The authors tackled the problem of identifying predictive biomarkers for subgroup analysis in medical research by proposing a generalized penalized regression method with a novel penalty function that enforces hierarchy between prognostic and predictive effects, resulting in a method that is asymptotically consistent and demonstrated as powerful in simulations and real case studies for discovering true biomarkers and identifying patient subgroups.
The identification of predictive biomarkers from a large scale of covariates for subgroup analysis has attracted fundamental attention in medical research. In this article, we propose a generalized penalized regression method with a novel penalty function, for enforcing the hierarchy structure between the prognostic and predictive effects, such that a nonzero predictive effect must induce its ancestor prognostic effects being nonzero in the model. Our method is able to select useful predictive biomarkers by yielding a sparse, interpretable, and predictable model for subgroup analysis, and can deal with different types of response variable such as continuous, categorical, and time-to-event data. We show that our method is asymptotically consistent under some regularized conditions. To minimize the generalized penalized regression model, we propose a novel integrative optimization algorithm by integrating the majorization-minimization and the alternating direction method of multipliers, which is named after \texttt{smog}. The enriched simulation study and real case study demonstrate that our method is very powerful for discovering the true predictive biomarkers and identifying subgroups of patients.