Source: Ann. Appl. Stat.
Volume 6, Number 1
Most of the available methods for longitudinal data analysis are
designed and validated for the situation where the number of
subjects is large and the number of observations per subject is
relatively small. Motivated by the Naturalistic Teenage Driving
Study (NTDS), which represents the exact opposite situation, we
examine standard and propose new methodology for marginal
analysis of longitudinal count data in a small number of very
long sequences. We consider standard methods based on
generalized estimating equations, under working independence or
an appropriate correlation structure, and find them
unsatisfactory for dealing with time-dependent covariates when
the counts are low. For this situation, we explore a
within-cluster resampling (WCR) approach that involves repeated
analyses of random subsamples with a final analysis that
synthesizes results across subsamples. This leads to a novel WCR
method which operates on separated blocks within subjects and
which performs better than all of the previously considered
methods. The methods are applied to the NTDS data and evaluated
in simulation experiments mimicking the NTDS.
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