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Statistical Methods in Medical Research, Vol. 8, No. 1, 51-83 (1999)
DOI: 10.1177/096228029900800105

Parametric models for incomplete continuous and categorical longitudinal data

M G Kenward

Institute of Mathematics and Statistics, The University of Kent, Canterbury, Kent, UK, m.g.kenward{at}ukc.ac.uk

G Molenberghs

Biostatistics, Limburgs Universiteit Centrum, Belgium

This paper reviews models for incomplete continuous and categorical longitudinal data. In terms of Rubin's classification of missing value processes we are specifically concerned with the problem of nonrandom missingness. A distinction is drawn between the classes of selection and pattern-mixture models and, using several examples, these approaches are compared and contrasted. The central roles of identifiability and sensitivity are emphasized throughout.


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