摘要：The generalized linear mixed effects model (GLMM) approach is widely used to analyze
longitudinal binary data when the goal of the study is a subject-specific interpretation
because it allows missing values on the response, provided they are missing
at random (MAR), and accounts the correlation among the repeated observations of
the same subject by the inclusion of random effects in the linear predictor. However,
in GLMM it is assumed that the observations of the same subject are independent
conditional to the random effects and covariates which may be not true. To overcome
this problem [9] extended this model using binary Markov chains as the basic
stochastic mechanism. The aim of this paper is to give a statistical assessment of
both approaches in terms of properties such as efficiency and coverage probability, as
well as, to give some guidelines for the choice of the statistical approach to an applied
researcher. Both procedures are described and a simulation study is carried out to
compare their performance. An analysis of a longitudinal binary data set illustrates
the performance of both procedures in a practical example. The R packages lme4 and
bild are used.
关键词：binary longitudinal data; exact likelihood; random effects; Markov chain; missing data
