Pattern-mixture models with proper time dependence


Kenward, MG; Molenberghs, G; Thijs, H; (2003) Pattern-mixture models with proper time dependence. Biometrika, 90 (1). pp. 53-71. ISSN 0006-3444 DOI: https://doi.org/10.1093/biomet/90.1.53

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Abstract

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 dropout 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.

Item Type: Article
Keywords: drop-out, longitudinal data, missing at random, missing data, repeated measurements, selection model, Longitudinal data, incomplete data, missing data, drop-out
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Web of Science ID: 181996800005
URI: http://researchonline.lshtm.ac.uk/id/eprint/17416

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