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

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