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