Abstract Number:
2833
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Poster
Participants:
Zheng Wang (1), Thomas Murray (2), Wenshan Han (3), Lifeng Lin (4), Lianne Siegel (2), Haitao Chu (5)
Institutions:
(1) Merck & Co., Inc., Rahway, NJ, (2) University of Minnesota, Minneapolis, MN, (3) Florida State University, Tallahassee, FL, (4) University of Arizona, Tucson, AZ, (5) Pfizer, Inc, New York, NY
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
While Network Meta-Analysis (NMA) facilitates simultaneous assessment of multiple treatments, challenges such as sparse direct comparisons among treatments persist, making accurate estimation of the correlation between multiple treatments in arm-based NMA (AB-NMA) challenging. To address these challenges and complement the analysis, we develop a novel sensitivity analysis tool tailored for AB-NMA: a tipping point analysis within the Bayesian framework, specifically targeting correlation parameters, to assess their influence on the robustness of conclusions about relative treatment effects, including changes in statistical significance and the magnitude of point estimates. Applying the analysis to multiple NMA datasets with 112 treatment pairs, we identified tipping points in 13 pairs (11.6%) for significance change, and in 29 pairs (25.9%) for magnitude change with a threshold at 15%. Our results underscore potential commonality in tipping points, emphasizing the necessity of our proposed analysis, especially in networks with sparse direct comparisons or wide credible intervals of estimated correlation.
Keywords:
network meta-analysis|correlation between multiple treatments|tipping point analysis|sensitivity analysis|robustness of research conclusion|statistical significance
Sponsors:
Section on Bayesian Statistical Science
Tracks:
Applications in Life Sciences and Medicine
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