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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/309

Title: An Application of Maximum Likelihood and Generalized Estimating Equations to the Analysis of Ordinal Data from a Longitudinal Study with Cases Missing at Random
Authors: Kenward, Michael
Lesaffre, Emmanuel
Molenberghs, Geert
Keywords: Multivariate data
Longitudinal data
Missing data
Categorical data
Issue Date: 1994
Citation: BIOMETRICS, 50(4), p. 945-953
Abstract: Data are analysed from a longitudinal psychiatric study in which there are no dropouts that do not occur completely at random. A marginal proportional odds model is fitted that relates the response (severity of side effects) to various covariates. Two methods of estimation are used: generalized estimating equations (GEE) and maximum likelihood (ML). Both the complete set of data and the data from only those subjects completing the study are analysed. For the completers-only data, the GEE and ML analyses produce very similar results. These results differ considerably from those obtained from the analyses of the full data set. There are also marked differences between the results obtained from the GEE and ML analysis of the full data set. The occurrence of such differences is consistent with the presence of a non-completely-random dropout process and it can be concluded in this example that both the analyses of the completers only and the GEE analysis of the full data set produce misleading conclusions about the relationships between the response and covariates
URI: http://hdl.handle.net/1942/309
DOI: 10.2307/2533434
ISI #: A1994QP05000004
ISSN: 0006-341X
Type: Journal Contribution
Appears in Collections: Research publications

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