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Activity Number: 313
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #319105
Title: Network Meta-Analysis of Multiple Factors
Author(s): Lifeng Lin* and Haitao Chu
Companies: University of Minnesota and University of Minnesota
Keywords: Bayesian inference ; Cholesky decomposition ; missing data ; multivariate hybrid model ; within-study correlation

In medical sciences, a disease is associated with multiple risk/protective factors. While many studies report multiple factors, nearly all meta-analyses separately synthesized the association between each factor and the disease condition of interest. Since each study may only report a subset of all factors, results from separate meta-analyses may not be comparable as each may use different subpopulation. This limits our ability to select most important factors for the design of a multifactor intervention program. From the perspective of missing data analysis, we propose a novel network meta-analysis method to jointly model multiple factors (NMA-MF) simultaneously. By borrowing information across multiple factors, NMA-MF greatly improves statistical efficiency and reduce potential biases. As within-study correlations between multiple factors are commonly unknown from published articles, we introduce a hybrid random-effects model to perform NMA-MF, which effectively accounts for both within- and between-study correlations. The performance of the proposed method is studied using simulations, and illustrated using a real dataset of 8 risk factors on pterygium synthesizing 29 studies.

Authors who are presenting talks have a * after their name.

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