Speaker
Description
In the era of precision medicine with increasing molecular information, the use of a multi-state model is required to capture the individual disease pathway along with underlying etiologies with greater precision. Especially the availability of big data with numerous covariates induces several statistical challenges for model building. For multi-state models based on high-dimensional data, effective modeling strategies are required to determine an optimal, ideally parsimonious model.
Standard methods integrate regularization into the fitting procedure to conduct variable selection. In the multi-state framework, linking covariate effects across transitions is needed to conduct joint variable selection. A useful technique to reduce model complexity is to address homogeneous covariate effects for distinct transitions. We integrate this approach to data-driven variable selection by extended regularization methods within multi-state model building. We propose the fused sparse-group lasso (FSGL) penalized Cox-type regression in the framework of multi-state models combining the penalization concepts of pairwise differences of covariate effects along with transition grouping. For optimization, we adapt the alternating direction method of multipliers (ADMM) algorithm to transition-specific hazards regression in the multi-state setting.
In a simulation study and application to acute myeloid leukemia (AML) data, we evaluate the algorithm's ability to select a sparse model incorporating relevant transition-specific effects and similar cross-transition effects of biomarkers. We investigate settings in which the combined penalty is beneficial compared to global lasso regularization.
Thus, effective model selection strategies in multi-state survival analysis are required for enhancing comprehension and interpretation of individual disease pathways, distinct oncological entities and tailored precision therapies, leading to improved personalized prognoses.