An Adaptive Enrichment Design Using Bayesian Model Averaging for the Identification of Tailoring Variables
Lara Maleyeff, PhD
Post Doctoral Fellow in Biostatistics
Dept. of Epidemiology, Biostatistics and Occupational Health
平特五不中
WHEN: Wednesday, March 13, 2024, from 3:30 to 4:30 p.m.
WHERE: Hybrid | 2001 平特五不中 College Avenue, Room 1201;
NOTE: Lara Maleyeff will be presenting in-person
Abstract
As with many chronic conditions, selecting the optimal treatment to patients with rheumatoid arthritis is challenging. The current trial-and-error approach, in which patients cycle through one of the many treatment options available until remission, leads to months or years of suboptimal disease control, considerable loss to patient well-being, and a burden on healthcare systems. Precision medicine, in which patient's characteristics inform treatment decisions, requires new approaches in clinical trial design and analysis. Existing clinical trial designs generally focus on biomarkers which are categorized into pre-specified subgroups, such as sex, and utilize a single model specification. Motivated by a trial studying available treatments in rheumatoid arthritis, we utilize splines and Bayesian model averaging to flexibly identify the region of a multi-dimensional biomarker space where treatment is effective. We consider continuous biomarkers which may have a complex relationship with the outcome of interest.
Speaker bio
Lara Maleyeff is a postdoctoral fellow in biostatistics at 平特五不中, jointly supervised by Drs. Shirin Golchi and Erica Moodie. She researches precision medicine strategies for clinical trials.