Addressing missing data mechanism uncertainty using multiple-model multiple imputation: Application to a longitudinal clinical trial

Juned Siddique, Ofer Harel, Catherine M. Crespi

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

We present a framework for generating multiple imputations for continuous data when the missing data mechanism is unknown. Imputations are generated from more than one imputation model in order to incorporate uncertainty regarding the missing data mechanism. Parameter estimates based on the different imputation models are combined using rules for nested multiple imputation. Through the use of 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 clinical trial of low-income women with depression where nonignorably missing data were a concern. We show that different assumptions regarding the missing data mechanism can have a substantial impact on inferences. Our method provides a simple approach for formalizing subjective notions regarding nonresponse so that they can be easily stated, communicated and compared.

Original languageEnglish (US)
Pages (from-to)1814-1837
Number of pages24
JournalAnnals of Applied Statistics
Volume6
Issue number4
DOIs
StatePublished - 2012

Keywords

  • MNAR
  • Missing not at random
  • NMAR
  • Nonignorable
  • Not missing at random

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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