An application of maximum likelihood and generalized estimating equations to the analysis of ordinal data from a longitudinal study with cases missing at random

A - Papers appearing in refereed journals

Kenward, M. G., Lesaffre, E. and Molenberghs, G. 1994. An application of maximum likelihood and generalized estimating equations to the analysis of ordinal data from a longitudinal study with cases missing at random. Biometrics. 50 (4), pp. 945-953. https://doi.org/10.2307/2533434

AuthorsKenward, M. G., Lesaffre, E. and Molenberghs, G.
Abstract

Data are analysed from a longitudinal psychiatric study in which there are 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 analyses 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.

KeywordsBiology; Mathematical & Computational Biology; Statistics & Probability
Year of Publication1994
JournalBiometrics
Journal citation50 (4), pp. 945-953
Digital Object Identifier (DOI)https://doi.org/10.2307/2533434
PubMed ID7787007
Open accessPublished as non-open access
Funder project or code101
206
Project: 141146
ISSN0006341X
PublisherWiley

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