Structure-preserving integrated analysis for risk stratification with application to cancer staging

To provide appropriate and practical level of health care, it is critical to group patients into relatively few strata that have distinct prognosis. Such grouping or stratification is typically based on well-established risk factors and clinical outcomes. 

This study from HIP Investigator Dr. Meggang Yu considers a statistical method for such grouping based on individual patient data from multiple studies. The method encourages a common grouping structure as a basis for borrowing information, but acknowledges data heterogeneity including unbalanced data structures across multiple studies. The study builds on the “lasso-tree” method that is more versatile than the well-known classification and regression tree method in generating possible grouping patterns.