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

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. Copyright © 2005 John Wiley & Sons, Ltd.