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.
Ma
Guoguang
G
Clinical Biostatistics, Merck & Co. Inc., BL3-2, Blue Bell, PA 19422, USA. guoguang_ma@merck.com
Troxel
Andrea B
AB
Heitjan
Daniel F
DF
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D000929:Antidepressive Agents, Tricyclic; D018931:Antineoplastic Agents, Hormonal; D008894:Milk Proteins; D005485:Flutamide; D003891:Desipramine