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

Mavridis, Dimitris; Salanti, Georgia; Furukawa, Toshi A; Cipriani, Andrea; Chaimani, Anna; White, Ian R (2019). Allowing for uncertainty due to missing and LOCF imputed outcomes in meta-analysis. Statistics in medicine, 38(5), pp. 720-737. Wiley-Blackwell 10.1002/sim.8009

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Social and Preventive Medicine (ISPM)

UniBE Contributor:

Salanti, Georgia

Subjects:

600 Technology > 610 Medicine & health
300 Social sciences, sociology & anthropology > 360 Social problems & social services

ISSN:

0277-6715

Publisher:

Wiley-Blackwell

Language:

English

Submitter:

Tanya Karrer

Date Deposited:

25 Oct 2018 12:04

Last Modified:

22 Oct 2019 23:33

Publisher DOI:

10.1002/sim.8009

PubMed ID:

30347460

Uncontrolled Keywords:

expert opinion informatively missing last observation carried forward pattern mixture model sensitivity analysis

BORIS DOI:

10.7892/boris.120644

URI:

https://boris.unibe.ch/id/eprint/120644

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