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Activity Number: 272 - Advances in Statistical Methods for Meta?Analysis
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 8:30 AM to 10:20 AM
Sponsor: General Methodology
Abstract #326775
Title: Multivariate Network Meta-Analysis to Mitigate Outcome Reporting Bias
Author(s): Stacia Marie DeSantis*
Companies: University of Texas Health Science Center at Houston
Keywords: network meta-analysis; Bayesian methods; missing data; multivariate meta-analysis
Abstract:

Outcome reporting bias (ORB) and publication bias (PB) are a threat to the validity of network meta-analysis (NMA). In recent years, multivariate meta-analysis (MMA) approaches have been proposed to handle the impact of potential ORB in the pairwise meta-analytic setting. These methods have shown that MMA can reduce bias and increases efficiency of pooled effect sizes. We present Bayesian methods for multivariate NMA (MNMA) that can reduce the effects of ORB and PB on pooled effect sizes. We perform several simulation studies that show MNMA reduces the bias of pooled effect sizes under a variety of outcome missingness scenarios, including missing at random and missing not at random. We apply the approaches to networks in alcohol use and depression.


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

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