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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/386

Title: Influence analysis to assess sensitivity of the dropout process
Authors: Molenberghs, Geert
Verbeke, Geert
Thijs, Herbert
Lesaffre, Emmanuel
Kenward, Michael
Keywords: Missing data
Longitudinal data
Issue Date: 2001
Citation: Computational Statistics and Data Analysis, 37(1). p. 93-113
Abstract: Diggle and Kenward (Appl. Statist. 43 (1994) 49) proposed a selection model for continuous longitudinal data subject to possible non-random dropout. It has provoked a large debate about the role for such models. The original enthusiasm was followed by skepticism about the strong but untestable assumption upon which this type of models invariably rests. Since then, the view has emerged that these models should ideally be made part of a sensitivity analysis. One of their examples is a set of data on mastitis in dairy cattle, about which they concluded that the dropout process was non-random. The same data were used in Kenward (Statist. Med. 17 (1998) 2723), who performed an informal sensitivity analysis. It thus presents an interesting opportunity for a formal sensitivity assessment, as proposed by Verbeke et al. (sensitivity analysis for non-random dropout: a local influence approach
URI: http://hdl.handle.net/1942/386
DOI: 10.1016/S0167-9473(00)00065-7
ISI #: 000170079000007
ISSN: 0167-9473
Category: A1
Type: Journal Contribution
Validation: ecoom, 2002
Appears in Collections: Research publications

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