| Summary: | Joint models for longitudinal and time-to-event data have been
applied in many different fields of statistics and clinical studies.
However, the main difficulty these models have to face with is the
computational problem. The requirement for numerical integration
becomes severe when the dimension of random effects increases.
In this paper, a modified two-stage approach has been proposed
to estimate the parameters in joint models. In particular, in the
first stage, the linear mixed-effects models and best linear unbiased
predictorsare applied to estimate parameters in the longitudinal
submodel. In the second stage, an approximation of the fully joint
log-likelihood is proposed using the estimated the values of these
parameters from the longitudinal submodel. Survival parameters are
estimated bymaximizing the approximation of the fully joint loglikelihood. Simulation studies show that the approach performs well,
especially when the dimension of random effects increases. Finally,
we implement this approach on AIDS data.
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