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Allowing for uncertainty due to missing and LOCF imputed outcomes in meta-analysis

Mavridis, D; Salanti, G; Furukawa, TA; Cipriani, A; Chaimani, A; White, IR; (2018) Allowing for uncertainty due to missing and LOCF imputed outcomes in meta-analysis. Statistics in Medicine 10.1002/sim.8009. Green open access

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Abstract

The use of the last observation carried forward (LOCF) method for imputing missing outcome data in randomized clinical trials has been much criticized and its shortcomings are well understood. However, only recently have published studies widely started using more appropriate imputation methods. Consequently, meta-analyses often include several studies reporting their results according to LOCF. The results from such meta-analyses are potentially biased and overprecise. We develop methods for estimating summary treatment effects for continuous outcomes in the presence of both missing and LOCF-imputed outcome data. Our target is the treatment effect if complete follow-up was obtained even if some participants drop out from the protocol treatment. We extend a previously developed meta-analysis model, which accounts for the uncertainty due to missing outcome data via an informative missingness parameter. The extended model includes an extra parameter that reflects the level of prior confidence in the appropriateness of the LOCF imputation scheme. Neither parameter can be informed by the data and we resort to expert opinion and sensitivity analysis. We illustrate the methodology using two meta-analyses of pharmacological interventions for depression.

Type: Article
Title: Allowing for uncertainty due to missing and LOCF imputed outcomes in meta-analysis
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/sim.8009
Publisher version: https://doi.org/10.1002/sim.8009
Language: English
Additional information: This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: expert opinion, informatively missing, last observation carried forward, pattern mixture model, sensitivity 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/10060361
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