An index of local sensitivity to nonignorable drop-out in longitudinal modelling

Guoguang Ma, Andrea B. Troxel, Daniel F. Heitjan

Research output: Contribution to journalArticle

51 Scopus citations

Abstract

In longitudinal studies with potentially nonignorable drop-out, one can assess the likely effect of the nonignorability in a sensitivity analysis. Troxel et al. proposed a general index of sensitivity to nonignorability, or ISNI, to measure sensitivity of key inferences in a neighbourhood of the ignorable, missing at random (MAR) model. They derived detailed formulas for ISNI in the special case of the generalized linear model with a potentially missing univariate outcome. In this paper, we extend the method to longitudinal modelling. We use a multivariate normal model for the outcomes and a regression model for the drop-out process, allowing missingness probabilities to depend on an unobserved response. The computation is straightforward, and merely involves estimating a mixed-effects model and a selection model for the drop-out, together with some simple arithmetic calculations. We illustrate the method with three examples.

Original languageEnglish (US)
Pages (from-to)2129-2150
Number of pages22
JournalStatistics in Medicine
Volume24
Issue number14
DOIs
StatePublished - Jul 30 2005

Keywords

  • Covariance structure
  • Ignorability
  • Longitudinal data
  • Missing data
  • Sensitivity analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Fingerprint Dive into the research topics of 'An index of local sensitivity to nonignorable drop-out in longitudinal modelling'. Together they form a unique fingerprint.

  • Cite this