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Biostatistics Seminar Series: 11/09, Monday 2pm, “Treatment ranking in Bayesian network meta-analysis and predictions” by Dr. Lifeng Lin of Florida State University.

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Dear Colleagues,

The Division of Biostatistics at the Department of Preventive Medicine invites you to attend the following seminar.

Date: Monday, 11/09/2020

Time: 2 P.M., Central Time (US and Canada)  

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Presenter: Dr. Lifeng Lin (Florida State University)

Title:Treatment ranking in Bayesian network meta-analysis and predictions

Abstract: Network meta-analysis (NMA) is an important tool to provide high-quality evidence about available treatments’ benefits and harms for comparative effectiveness research. Compared with conventional meta-analyses that synthesize related studies for pairs of treatments separately, an NMA uses both direct and indirect evidence to simultaneously compare all available treatments for a certain disease. It is of primary interest for clinicians to rank these treatments and select the optimal ones for patients. Various methods have been proposed to evaluate treatment ranking; among them, the mean rank, the so-called surface under the cumulative ranking curve (SUCRA), and P-score are widely used in current practice of NMAs. However, these measures only summarize treatment ranks among the studies collected in the NMA. Due to heterogeneity between studies, they cannot predict treatment ranks in a future study and thus may not be directly applied to healthcare for new patients. We propose innovative measures to predict treatment ranks by accounting for the heterogeneity between the existing studies in an NMA and a new study. They are the counterparts of the mean rank, SUCRA, and P-score under the new study setting. We use illustrative examples and simulation studies to evaluate the performance of the proposed measures.

 

About the speaker:
Dr. Lifeng Lin is an Assistant Professor in the Department of Statistics at Florida State University. His research focuses on statistical methods for meta-analysis, network meta-analysis of multiple-treatment comparisons, and publication bias. He is also interested in applications of Bayesian methods to real-world problems and approaches to improving research replicability.

(Google Scholar profile: https://scholar.google.com/citations?user=JJnpBesAAAAJ&hl=en)

We look forward to seeing you all among us. 

Chi-Yang Chiu, Ph.D.

Assistant Professor of Biostatistics

Department of Preventive Medicine, UTHSC