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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2933

Title: Obtaining marginal estimates from conditional categorical repeated measurements models with missing data
Authors: LINDSEY, James
Issue Date: 2000
Citation: STATISTICS IN MEDICINE, 19(6). p. 801-809
Abstract: The most commonly used models for categorical repeated measurement data are log-linear models. Not only are they easy to fit with standard software but they include such useful models as Markov chains and graphical models. However, these are conditional models and one often also requires the marginal probabilities of responses, for example, at each time point in a longitudinal study. Here a simple method of matrix manipulation is used to derive the maximum likelihood estimates of the marginal probabilities from any such conditional categorical repeated measures model. The technique is applied to the classical Muscatine data set, taking into account the dependence of missingness on previous observed values, as well as serial dependence and a random effect. Copyright (C) 2000 John Wiley & Sons, Ltd.
Notes: Limburgs Univ Ctr, Dept Biostat, B-3590 Diepenbeek, Belgium.Lindsey, JK, Limburgs Univ Ctr, Dept Biostat, Univ Campus, B-3590 Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/2933
ISI #: 000086328900006
ISSN: 0277-6715
Category: A1
Type: Journal Contribution
Validation: ecoom, 2001
Appears in Collections: Research publications

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