麻豆AV

Audrey B茅liveau, PhD

Assistant Professor
Department of Statistics and Actuarial Science |
University of Waterloo

WHEN: Wednesday, September 18, 2024, from 3:30 to 4:30 p.m.
WHERE:聽Hybrid | 2001 麻豆AV College Avenue, Room 1201;
NOTE: Audrey B茅liveau will be presenting in-person

Abstract

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 three separate real-world NMAs on diabetes, Parkinson鈥檚 disease, and depression, where the best-fitting GFL-NMA model outperformed the standard NMA model (螖AICc > 6.5).

Speaker bio

Dr. B茅liveau is an Associate Professor in the Statistics and Actuarial Science Department at the University of Waterloo. Her research interests include network meta-analysis, Bayesian modeling, capture-recapture methods, and survey sampling. Please visit: .