Student seminar series
Audrey Béliveau
Associate Professor, University of Waterloo
Room: M3 3127
Generalized Fused Lasso for Improving the Parsimony of Network Meta-Analysis Models
Network meta-analysis (NMA) is a valuable statistical tool for combining evidence on the comparative efficacy and safety of medical treatments from multiple studies. In this work, we develop a penalization framework for NMA that penalizes all pairwise differences between treatments using a generalized fused lasso (GFL). This approach improves model parsimony, resulting in more precise estimates of treatment differences and increased statistical power. Practical advantages include: 1) no prior knowledge of the similarity of treatment effects is required, 2) treatments are only assigned separate ranks if there is sufficient evidence to suggest they are different, and 3) computing time is minimal. The novel GFL-NMA method is successfully applied to two real-world NMAs on diabetes and Parkinson’s disease, with the best-fitting GFL-NMA model outperforming the standard NMA model (ΔAICc > 9.2).