Itemwise conditionally independent nonresponse modelling for incomplete multivariate data

Journal Article (Journal Article)

We introduce a nonresponse mechanism for multivariate missing data in which each study variable and its nonresponse indicator are conditionally independent given the remaining variables and their nonresponse indicators. This is a nonignorable missingness mechanism, in that nonresponse for any item can depend on values of other items that are themselves missing. We show that under this itemwise conditionally independent nonresponse assumption, one can define and identify nonparametric saturated classes of joint multivariate models for the study variables and their missingness indicators.We also showhowto perform sensitivity analysis with respect to violations of the conditional independence assumptions encoded by this missingness mechanism. We illustrate the proposed modelling approach with data analyses.

Full Text

Duke Authors

Cited Authors

  • Sadinle, M; Reiter, JP

Published Date

  • March 1, 2017

Published In

Volume / Issue

  • 104 / 1

Start / End Page

  • 207 - 220

Electronic International Standard Serial Number (EISSN)

  • 1464-3510

International Standard Serial Number (ISSN)

  • 0006-3444

Digital Object Identifier (DOI)

  • 10.1093/biomet/asw063

Citation Source

  • Scopus