title: Testing moderation in network meta-analysis with individual participant data.
abstract: Meta-analytic methods for combining data from multiple intervention trials are commonly used to estimate the effectiveness of an intervention. They can also be extended to study comparative effectiveness, testing which of several alternative interventions is expected to have the strongest effect. This often requires network meta-analysis (NMA), which combines trials involving direct comparison of two interventions within the same trial and indirect comparisons across trials. In this paper, we extend existing network methods for main effects to examining moderator effects, allowing for tests of whether intervention effects vary for different populations or when employed in different contexts. In addition, we study how the use of individual participant data may increase the sensitivity of NMA for detecting moderator effects, as compared with aggregate data NMA that employs study-level effect sizes in a meta-regression framework. A new NMA diagram is proposed. We also develop a generalized multilevel model for NMA that takes into account within-trial and between-trial heterogeneity and can include participant-level covariates. Within this framework, we present definitions of homogeneity and consistency across trials. A simulation study based on this model is used to assess effects on power to detect both main and moderator effects. Results show that power to detect moderation is substantially greater when applied to individual participant data as compared with study-level effects. We illustrate the use of this method by applying it to data from a classroom-based randomized study that involved two sub-trials, each comparing interventions that were contrasted with separate control groups. Copyright © 2016 John Wiley & Sons, Ltd.
authors: Department of Epidemiology and Biostatistics, College of Public Health MDC 56, University of South Florida, Tampa, U.S.A.
authors: Getachew A
authors: GA
authors: Dagne
authors: Department of Psychiatry and Behavioral Sciences, Department of Preventive Medicine, and Department of Medical Social Sciences, Northwestern University, Feinberg School of Medicine, Chicago, U.S.A.
authors: C Hendricks
authors: CH
authors: Brown
authors: Department of Psychology and Department of Psychiatry and Behavioral Sciences, George Washington University, Washington, DC, U.S.A.
authors: George
authors: G
authors: Howe
authors: Department of Mental Health, Bloomberg School of Public Health Johns Hopkins University, Baltimore, U.S.A.
authors: Sheppard G
authors: SG
authors: Kellam
authors: Department of Preventive Medicine, Department of Psychiatry and Behavioral Sciences, Northwestern University, Feinberg School of Medicine, Chicago, U.S.A.
authors: Lei
authors: L
authors: Liu
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D012107:Research Design; D000071076:Network Meta-Analysis as Topic
keywords: integrative data analysis; meta-analysis; moderation; network meta-analysis; participant level data; statistical power