title: Selection models for repeated measurements with non-random dropout: an illustration of sensitivity.
abstract: 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.
authors.affiliation: Institute of Mathematics and Statistics, University of Kent at Canterbury, U.K. m.g.kenward@ukc.ac.uk
authors.forename: M G
authors.initials: MG
authors.lastname: Kenward
mesh_terms: D000818:Animals; D002417:Cattle; D003612:Dairying; D005260:Female; D008414:Mastitis, Bovine / Q000150:complications / Q000453:epidemiology*; D015233:Models, Statistical; D012680:Sensitivity and Specificity