Allowing for uncertainty due to missing and LOCF imputed outcomes in meta-analysis.
expert opinion
informatively missing
last observation carried forward
pattern mixture model
sensitivity analysis
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
28 02 2019
28 02 2019
Historique:
received:
17
10
2017
revised:
25
09
2018
accepted:
27
09
2018
pubmed:
23
10
2018
medline:
2
5
2020
entrez:
23
10
2018
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 30347460
doi: 10.1002/sim.8009
pmc: PMC6492186
doi:
Substances chimiques
Antidepressive Agents
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
720-737Subventions
Organisme : Medical Research Council
ID : MC_UU_12023/21
Pays : United Kingdom
Informations de copyright
© 2018 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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