Introduction to Meta-Analysis

Theodoros Evrenoglou (), Silvia Metelli () and Anna Chaimani ()
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Theodoros Evrenoglou: Université de Paris, Research Center of Epidemiology and Statistics (CRESS-U1153), INSERM
Silvia Metelli: Université de Paris, Research Center of Epidemiology and Statistics (CRESS-U1153), INSERM
Anna Chaimani: Université de Paris, Research Center of Epidemiology and Statistics (CRESS-U1153), INSERM

Chapter 110 in Principles and Practice of Clinical Trials, 2022, pp 2179-2195 from Springer

Abstract: Abstract Studies within a systematic review are often combined statistically in a meta-analysis, which quantitatively synthesizes all available evidence about the relative effects of two healthcare interventions for the same clinical outcome. A key issue in every meta-analysis is whether the identified randomized control trials (RCTs) are similar enough to be combined together since important differences in trial- or patient-level characteristics may affect the treatment effects. Such differences, called heterogeneity, need to be properly investigated and accounted for in the analysis. In this chapter, we introduce the basic concepts of meta-analyses of RCTs and we describe the two main meta-analytical models, namely, the common effect and the random effects models. Then, we present several ways to identify and assess heterogeneity. We discuss the interpretation of results from a meta-analysis using two exemplar datasets. The chapter closes with a brief introduction to more sophisticated meta-analytical techniques such as the use of individual participant data and network meta-analysis.

Keywords: Evidence synthesis; Heterogeneity; Random effects; Publication bias (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/978-3-319-52636-2_287

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