Multiple imputation for handling systematically missing confounders in meta-analysis of individual participant data

PROG-IMT Study Group

Research output: Contribution to journalArticle

45 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)4890-4905
Number of pages16
JournalStatistics in medicine
Volume32
Issue number28
Early online date16 Jul 2013
DOIs
Publication statusPublished - 10 Dec 2013

Keywords

  • Cardiovascular Diseases/epidemiology
  • Carotid Intima-Media Thickness
  • Computer Simulation
  • Confounding Factors (Epidemiology)
  • Epidemiologic Methods
  • Humans
  • Meta-Analysis as Topic
  • Models, Statistical
  • Monte Carlo Method

Cite this