| Summary: | Drop-outs are a common problem in longitudinal studies. In terms of statistical
models for the data, there are three types of drop-out mechanisms: drop-out occurring
completely at random (CRD), drop-out occurring at random (RD) and informative
drop-out (ID). The drop-out mechanism is classified as CRD if the drop-out
mechanism is independent of the measurements; as RD if the drop-out mechanism
depends only on the observed but not the unobserved measurements, and as ID if
the drop-out mechanism depends on both the observed and unobserved measurements.
CRD and RD are referred to as ignorable because the drop-out mechanism
can be ignored for the purpose of making inferences about the observed measurements,
while ID is non-ignorable. Analyses based on an assumption of ignorable
drop-out, when in reality the drop-out mechanism is non-ignorable, can lead to misleading
or biased results. Likelihood-based models for continuous and categorical
longitudinal data subject to non-ignorable drop-out have been developed. In this
thesis, we focus on exploring likelihood-based models for binary longitudinal data
subject to informative drop-out.
The two modelling approaches considered are a selection model proposed by
Baker (1995) and a transition model proposed by Liu et al. (1999). We apply these
models to a data set from a multiple sclerosis (MS) clinical trial. The aims of the
analyses are to investigate whether there is an indication of informative drop-out in
this data, and to assess the sentivity of inferences concerning the treatment effects
to the underlying drop-out mechanisms. We do not attempt to provide a definitive
analyses of the data set, but rather to explore a variety of models which incorporate
informative drop-out. === Science, Faculty of === Statistics, Department of === Graduate
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