Parametric models for incomplete continuous and categorical longitudinal data

Kenward, Michael G. and Molenberghs, G. (1999) Parametric models for incomplete continuous and categorical longitudinal data. Review of: Parametric models for incomplete continuous and categorical longitudinal data. by Kenward, Michael G. and Molenberghs, G.. Statistical Methods in Medical Research, 8 (1). pp. 51-83. ISSN 0962-2802. (The full text of this publication is not available from this repository)

The full text of this publication is not available from this repository. (Contact us about this Publication)
Official URL
http://smm.sagepub.com/cgi/content/refs/8/1/51

Abstract

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.

Item Type: Review
Subjects: Q Science
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science
Faculties > Science Technology and Medical Studies > Kent Institute of Medicine and Health Sciences (KIMHS)
Depositing User: I.T. Ekpo
Date Deposited: 20 Jun 2009 05:20
Last Modified: 11 Jul 2014 09:19
Resource URI: http://kar.kent.ac.uk/id/eprint/16888 (The current URI for this page, for reference purposes)
  • Depositors only (login required):