Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data
Resche-Rigon M (1), White IR, Bartlett JW, Peters SA, Thompson SG; PROG-IMT Study Group.
(1) MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, U.K.; DBIM, Hôpital Saint-Louis, APHP, Paris, France; Université Paris Diderot, Paris, France; Inserm UMRS 717, Paris, France.
Revisão:Statistics in Medicine
Data: 10/Dez/2013Unidad de Chequeos [ES]
A variable is 'systematically missing' if it is missing for all individuals within particular studies in an individual participant data meta-analysis. When a systematically missing variable is a potential confounder in observational epidemiology, standard methods either fail to adjust the exposure-disease association for the potential confounder or exclude studies where it is missing.
We propose a new approach to adjust for systematically missing confounders based on multiple imputation by chained equations. Systematically missing data are imputed via multilevel regression models that allow for heterogeneity between studies.
A simulation study compares various choices of imputation model. An illustration is given using data from eight studies estimating the association between carotid intima media thickness and subsequent risk of cardiovascular events.
Results are compared with standard methods and also with an extension of a published method that exploits the relationship between fully adjusted and partially adjusted estimated effects through a multivariate random effects meta-analysis model.
We conclude that multiple imputation provides a practicable approach that can handle arbitrary patterns of systematic missingness. Bias is reduced by including sufficient between-study random effects in the imputation model.
CITAÇÃO DO ARTIGO Stat Med. 2013 Dec 10;32(28):4890-905. doi: 10.1002/sim.5894. Epub 2013 Jul 16.
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