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
[email protected]
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Dimitris Mavridis
Department of Primary Education,
School of Education
University of Ioannina
Ioannina, Greece
[email protected]
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Julian P. T. Higgins
Population Health Sciences,
Bristol Medical School
University of Bristol
Bristol, UK
[email protected]
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Georgia Salanti
Institute of Social and Preventive Medicine
University of Bern
Bern, Switzerland
[email protected]
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Ian R. White
MRC Biostatistics Unit
Cambridge, UK
and
MRC Clinical Trials Unit at UCL
London, UK
[email protected]
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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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