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Biometrika 2003 90(1):53-71; doi:10.1093/biomet/90.1.53
© 2003 by Biometrika Trust
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Pattern-mixture models with proper time dependence

M.G. Kenward1, G.Molenberghs2 and H.Thijs2

1 Medical Statistics Unit, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, U.Kmike.kenward{at}lshtm.ac.uk 2 Biostatistics, CenStat, Limburgs Universitair Centrum, Universitaire Campus, B-3590 Diepenbeek, Belgium geert.molenberghs{at}luc.ac.be herbert.thijs{at}luc.ac.be

Recently, pattern-mixture modelling has become a popular tool for modelling incomplete longitudinal data.Such models are under-identified in the sense that, for any drop-out pattern, the data provide no direct information on the distribution of the unobserved outcomes, given the observed ones. One simple way of overcoming this problem, ordinary extrapolation of sufficiently simple pattern-specific models, often produces rather unlikely descriptions; several authors consider identifying restrictions instead. Molenberghs et al. (1998) have constructed identifying restrictions corresponding to missing at random. In this paper, the family of restrictions where drop-out does not depend on future, unobserved observations is identified. The ideas are illustrated using a clinical study of Alzheimer patients.

Key Words: Drop-out; Longitudinal data; Missing at random; Missing data; Repeated measurements; Selection model

Received June 2000. Revised June 2002


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