Article type
Year
Abstract
Objectives:
To understand the statistical methodology of network meta-analyses and the assumption of consistency.
Description:
This is the second of two workshops about how to conduct Cochrane Reviews that aim to compare more than two interventions; this workshop focuses on statistical methodology for network meta-analysis. Network meta-analysis is the statistical methodology used to combine evidence in a network of trials that compare more than two interventions. The workshop will provide insight to network meta-analysis models that can be used to derive estimates for the relative effects of all treatments of interest. We will explore the different implementation alternatives through worked examples and we will discuss the underlying assumption of consistency extensively. We will present approaches to check for, and incorporate, inconsistency in the results and we will present applications of multiple-treatment meta-regression models. Finally, we will discuss concerns regarding the role of bias in network meta-analysis.
To understand the statistical methodology of network meta-analyses and the assumption of consistency.
Description:
This is the second of two workshops about how to conduct Cochrane Reviews that aim to compare more than two interventions; this workshop focuses on statistical methodology for network meta-analysis. Network meta-analysis is the statistical methodology used to combine evidence in a network of trials that compare more than two interventions. The workshop will provide insight to network meta-analysis models that can be used to derive estimates for the relative effects of all treatments of interest. We will explore the different implementation alternatives through worked examples and we will discuss the underlying assumption of consistency extensively. We will present approaches to check for, and incorporate, inconsistency in the results and we will present applications of multiple-treatment meta-regression models. Finally, we will discuss concerns regarding the role of bias in network meta-analysis.