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The Stata Journal
Volume 18 Number 3: pp. 716-740

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Allowing for informative missingness in aggregate data meta-analysis with continuous or binary outcomes: Extensions to metamiss

Anna Chaimani
Paris Descartes University;
INSERM, UMR1153 Epidemiology and Statistics,
Sorbonne Paris Cité Research Center (CRESS), METHODS Team;
Cochrane France
Paris, France
Dimitris Mavridis
Department of Primary Education,
School of Education
University of Ioannina
Ioannina, Greece
Julian P. T. Higgins
Population Health Sciences,
Bristol Medical School
University of Bristol
Bristol, UK
Georgia Salanti
Institute of Social and Preventive Medicine
University of Bern
Bern, Switzerland
Ian R. White
MRC Biostatistics Unit
Cambridge, UK
MRC Clinical Trials Unit at UCL
London, UK
Abstract.  Missing outcome data can invalidate the results of randomized trials and their meta-analysis. However, addressing missing data is often a challenging issue because it requires untestable assumptions. The impact of missing outcome data on the meta-analysis summary effect can be explored by assuming a relationship between the outcome in the observed and the missing participants via an informative missingness parameter. The informative missingness parameters cannot be estimated from the observed data, but they can be specified, with associated uncertainty, using evidence external to the meta-analysis, such as expert opinion. The use of informative missingness parameters in pairwise meta-analysis of aggregate data with binary outcomes has been previously implemented in Stata by the metamiss command. In this article, we present the new command metamiss2, which is an extension of metamiss for binary or continuous data in pairwise or network meta-analysis. The command can be used to explore the robustness of results to different assumptions about the missing data via sensitivity analysis.

View all articles by these authors: Anna Chaimani, Dimitris Mavridis, Julian P. T. Higgins, Georgia Salanti, Ian R. White

View all articles with these keywords: metamiss2, informative missingness, mixed treatment comparison, sensitivity analysis, meta-analysis

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