Binary variable multiple-model multiple imputation to address missing data mechanism uncertainty: Application to a smoking cessation trial

Juned Siddique*, Ofer Harel, Catherine M. Crespi, Donald Hedeker

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

The true missing data mechanism is never known in practice. We present a method for generating multiple imputations for binary variables, which formally incorporates missing data mechanism uncertainty. Imputations are generated from a distribution of imputation models rather than a single model, with the distribution reflecting subjective notions of missing data mechanism uncertainty. Parameter estimates and standard errors are obtained using rules for nested multiple imputation. Using simulation, we investigate the impact of missing data mechanism uncertainty on post-imputation inferences and show that incorporating this uncertainty can increase the coverage of parameter estimates. We apply our method to a longitudinal smoking cessation trial where nonignorably missing data were a concern. Our method provides a simple approach for formalizing subjective notions regarding nonresponse and can be implemented using existing imputation software.

Original languageEnglish (US)
Pages (from-to)3013-3028
Number of pages16
JournalStatistics in Medicine
Volume33
Issue number17
DOIs
StatePublished - Jul 30 2014

Keywords

  • Binary data
  • NMAR
  • Nonignorable
  • Not missing at random

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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