Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model

Shaun R. Seaman*, Rachael A. Hughes

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

7 Citations (Scopus)
253 Downloads (Pure)

Abstract

Estimating the parameters of a regression model of interest is complicated by missing data on the variables in that model. Multiple imputation is commonly used to handle these missing data. Joint model multiple imputation and full-conditional specification multiple imputation are known to yield imputed data with the same asymptotic distribution when the conditional models of full-conditional specification are compatible with that joint model. We show that this asymptotic equivalence of imputation distributions does not imply that joint model multiple imputation and full-conditional specification multiple imputation will also yield asymptotically equally efficient inference about the parameters of the model of interest, nor that they will be equally robust to misspecification of the joint model. When the conditional models used by full-conditional specification multiple imputation are linear, logistic and multinomial regressions, these are compatible with a restricted general location joint model. We show that multiple imputation using the restricted general location joint model can be substantially more asymptotically efficient than full-conditional specification multiple imputation, but this typically requires very strong associations between variables. When associations are weaker, the efficiency gain is small. Moreover, full-conditional specification multiple imputation is shown to be potentially much more robust than joint model multiple imputation using the restricted general location model to mispecification of that model when there is substantial missingness in the outcome variable.

Original languageEnglish
Pages (from-to)1603-1614
Number of pages12
JournalStatistical Methods in Medical Research
Volume27
Issue number6
Early online date5 Sep 2016
DOIs
Publication statusPublished - Jun 2018

Keywords

  • chained equations
  • Compatibility
  • congeniality
  • Gibbs sampler
  • informative margins
  • linear discriminant analysis
  • log linear model
  • missing data

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