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Activity Number: 418 - Recent Developments in Network Meta-Analysis
Type: Invited
Date/Time: Thursday, August 12, 2021 : 4:00 PM to 5:50 PM
Sponsor: ENAR
Abstract #314416
Title: Assessment of Homogeneity and Consistency for Network Meta-Analysis
Author(s): Joseph G Ibrahim* and Cheng Zhang and Hao Li and Ming-Hui Chen and Arvind Shah and Jianxin Lin
Companies: UNC and UCONN and Boeringer-Ingelheim and UCONN and Merck, Inc. and Merck, Inc.
Keywords: Plausibility Index; Consistency; Homogeneity; Contrast Matrix; Bayesian methods; Fixed Effects Model

One of the long-standing methodological issues in network meta analysis (NMA) is that of assessing homogeneity and consistency in treatment comparisons. However, the consistency assumption may not hold in the presence of heterogeneity. In this paper, we construct general linear hypotheses to investigate homogeneity and consistency under a general fixed effects model. An algorithm is developed to compute all the inconsistency testable loops in network meta-data, as well as the contrast matrices under homogeneity and consistency assumptions. Under the normal fixed effects model, we show the equivalence of the likelihood ratio test (LRT) under the proposed linear hypotheses and Bucher's method for testing inconsistency based on comparison of the weighted averages of direct and indirect treatment effects. A novel Plausibility Index (PI) is developed to assess homogeneity and consistency. Theoretical properties of the proposed methodology are examined in details. A road map for ranking treatments is further proposed. We apply the proposed methodology to analyze the network meta data from 29 randomized clinical trials with 34 active dose-level treatment arms plus placebo (PBO).

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

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