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A comparison of likelihood‐based and marginal estimating equation methods for analysing repeated ordered categorical responses with missing data: Application to an intervention trial of vitamin prophylaxis for oesophageal dysplasia
Author(s) -
Mark Steven D.,
Gail Mitchell H.
Publication year - 1994
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780130511
Subject(s) - categorical variable , missing data , statistics , restricted maximum likelihood , inference , mathematics , standard error , generalized estimating equation , marginal model , sample size determination , computer science , econometrics , maximum likelihood , artificial intelligence , regression analysis
The purpose of this research was to develop appropriate methods for analysing repeated ordinal categorical data that arose in an intervention trial to prevent oesophageal cancer. The measured response was the degree of oesophageal dysplasia at 2.5 and 6 years after randomization. An important feature was that some response measurements were missing, and the missingness was not ‘completely at random’ (MCAR). We show that standard likelihood‐based methods and standard methods based on marginal estimating equations yield biased results, and we propose adaptations to both these approaches that yield valid inference under the weaker ‘missing at random’ (MAR) assumption. On the basis of efficiency calculations, simulation studies of finite sample properties, ease of computation, and flexibility for testing and exploring a range of treatment models, we recommend the adapted likelihood‐based approach for problems of this type, in which there are abundant data for estimating parameters.