The nature of sensitivity in monotone missing not at random models

Jansen, I; Hens, N; Molenberghs, G; Aerts, M; Verbeke, G; Kenward, MG; (2006) The nature of sensitivity in monotone missing not at random models. Computational statistics & data analysis, 50 (3). pp. 830-858. ISSN 0167-9473 DOI:

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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. (c) 2004 Elsevier B.V. All rights reserved.

Item Type: Article
Keywords: ignorability, likelihood ratio test, linear mixed model, local, influence, missing at random, missing not at random, sensitivity, analysis, Pattern-mixture models, incomplete data, longitudinal data, categorical-data, clinical-trials, dropout process, local influence, inference, truncation, statistics
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Web of Science ID: 232738400016


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