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

Chaimani, A; Mavridis, D; Higgins, JPT; Salanti, G; White, IR; (2018) Allowing for informative missingness in aggregate data meta-analysis with continuous or binary outcomes: Extensions to metamiss. The Stata Journal , 18 (3) pp. 716-740. Green open access

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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.

Type: Article
Title: Allowing for informative missingness in aggregate data meta-analysis with continuous or binary outcomes: Extensions to metamiss
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.stata-journal.com/article.html?article...
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: st0540, metamiss2, informative missingness, mixed treatment comparison, sensitivity analysis, meta-analysis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology
URI: https://discovery.ucl.ac.uk/id/eprint/10057606
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