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 language | English (US) |
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Pages (from-to) | 2129-2150 |
Number of pages | 22 |
Journal | Statistics in Medicine |
Volume | 24 |
Issue number | 14 |
DOIs | |
State | Published - Jul 30 2005 |
Keywords
- Covariance structure
- Ignorability
- Longitudinal data
- Missing data
- Sensitivity analysis
ASJC Scopus subject areas
- Epidemiology
- Statistics and Probability