eprintid: 10075044 rev_number: 29 eprint_status: archive userid: 608 dir: disk0/10/07/50/44 datestamp: 2019-05-30 14:10:13 lastmod: 2021-12-13 03:04:45 status_changed: 2019-10-23 17:20:54 type: article metadata_visibility: show creators_name: Mavridis, D creators_name: White, IR title: Dealing with missing outcome data in meta-analysis ispublished: inpress divisions: UCL divisions: B02 divisions: D65 keywords: informative missingness odds ratio, informative missingness parameter, meta-analysis, missing data, missing not at random note: Copyright © 2019 The Authors Research Synthesis Methods Published by John Wiley & Sons LtdThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. abstract: Missing data result in less precise and possibly biased effect estimates in single studies. Bias arising from studies with incomplete outcome data is naturally propagated in a meta-analysis. Conventional analysis using only individuals with available data is adequate when the meta-analyst can be confident that the data are missing at random (MAR) in every study - that is, that the probability of missing data does not depend on unobserved variables, conditional on observed variables. Usually such confidence is unjustified as participants may drop out due to lack of improvement or adverse effects. The MAR assumption cannot be tested and a sensitivity analysis to assess how robust results are to reasonable deviations from the MAR assumption is important. Two methods may be used based on plausible alternative assumptions about the missing data. Firstly, the distribution of reasons for missing data may be used to impute the missing values. Secondly, the analyst may specify the magnitude and uncertainty of possible departures from the missing at random assumption, and these may be used to correct bias and re-weight the studies. This is achieved by employing a pattern mixture model and describing how the outcome in the missing participants is related to the outcome in the completers. Ideally, this relationship is informed using expert opinion. The methods are illustrated in two examples with binary and continuous outcomes. We provide recommendations on what trial investigators and systematic reviewers should do to minimise the problem of missing outcome data in meta-analysis. date: 2019 date_type: published official_url: https://doi.org/10.1002/jrsm.1349 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1649336 doi: 10.1002/jrsm.1349 lyricists_name: White, Ian lyricists_id: IWWHI35 actors_name: Flynn, Bernadette actors_id: BFFLY94 actors_role: owner full_text_status: public publication: Research Synthesis Methods event_location: England issn: 1759-2887 citation: Mavridis, D; White, IR; (2019) Dealing with missing outcome data in meta-analysis. Research Synthesis Methods 10.1002/jrsm.1349 <https://doi.org/10.1002/jrsm.1349>. (In press). Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10075044/1/White_Mavridis_et_al-2019-Research_Synthesis_Methods.pdf