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Selection models for repeated measurements with non‐random dropout: an illustration of sensitivity
Author(s) -
Kenward Michael G.
Publication year - 1998
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19981215)17:23<2723::aid-sim38>3.0.co;2-5
Subject(s) - dropout (neural networks) , sensitivity (control systems) , selection (genetic algorithm) , statistics , model selection , computer science , random effects model , simple (philosophy) , econometrics , mathematics , machine learning , medicine , engineering , philosophy , meta analysis , epistemology , electronic engineering
The outcome‐based selection model of Diggle and Kenward for repeated measurements with non‐random dropout is applied to a very simple example concerning the occurrence of mastitis in dairy cows, in which the occurrence of mastitis can be modelled as a dropout process. It is shown through sensitivity analysis how the conclusions concerning the dropout mechanism depend crucially on untestable distributional assumptions. This example is exceptional in that from a simple plot of the data two outlying observations can be identified that are the source of the apparent evidence for non‐random dropout and also provide an explanation of the behaviour of the sensitivity analysis. It is concluded that a plausible model for the data does not require the assumption of non‐random dropout. © 1998 John Wiley & Sons, Ltd.