The nature of sensitivity in monotone missing not at random models

Authors:
Ivy Jansen;Niel Hens;Geert Molenberghs;Marc Aerts;Geert Verbeke;Michael G. Kenward
Affiliations:
Center for Statistics, Limburgs Universitair Centrum, Universitaire Campus Building D, 3590 Diepenbeek, Belgium;Center for Statistics, Limburgs Universitair Centrum, Universitaire Campus Building D, 3590 Diepenbeek, Belgium;Center for Statistics, Limburgs Universitair Centrum, Universitaire Campus Building D, 3590 Diepenbeek, Belgium;Center for Statistics, Limburgs Universitair Centrum, Universitaire Campus Building D, 3590 Diepenbeek, Belgium;Biostatistical Centre, Katholieke Universiteit Leuven, Belgium;Medical Statistics Unit, London School of Hygiene and Tropical Medicine, UK
Venue:
Computational Statistics & Data Analysis
Year:
2006

Citing 3
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Abstract

Models for incomplete longitudinal data under missingness not at random have gained some popularity. At the same time, cautionary remarks have been issued regarding their sensitivity to often unverifiable modeling assumptions. Consequently, there is evidence for a shift towards using ignorable methodology, supplemented with sensitivity analyses to explore the impact of potential deviations of this assumption in the direction of missingness at random. One such tool is local influence. It is shown that local influence tends to pick up a lot of different anomalies in the data at hand, not just deviations in the MNAR mechanism. This particular behavior is described and insight offered in terms of the non-standard behavior of the likelihood ratio test statistic for MAR missingness versus MNAR missingness within a model of the Diggle and Kenward type.