The assessment of quality of life as a primary outcome
in
cancer
clinical trials
is
now almost
universal. Such data are necessarily longitudinal and multidimensional, and
are often severely
unbalanced by missing values or early patient death. However, to
date, their
reporting
in the
applied literature has generally used simple descriptive summaries
that
ignore
many
of these
complexities. Not only can these be misleading,
but they
generally
do
not
allow
firm
conclusions to be drawn about a major endpoint.
The
aim
of this thesis
is to
assess the
practical
application of recent developments in statistical methodology
for the
analysis of quality
of
life
data collected using self assessment questionnaires within
cancer clinical trials. Its
emphasis
is on the use of relatively simple and flexible tools that
will
allow more reliable
and powerful
inferences to be drawn from the data than is done at present.
The principal statistical tools considered are random
coefficient and
marginal models.
It
is shown that these can be successfully used for the
analysis
of continuous,
binary
and
ordinal
responses. In particular, they offer a simple approach to the
analysis of repeated
multivariate
outcomes and can be very easily extended to model the
complex patterns
of response that
are
often seen in following cancer treatment.
In relation to the problem of censored quality
of
life
as a result of patient
death,
analyses
that attempt to combine the survival and quality
of
life
endpoints
in
a single variable are
contrasted with those that consider the two endpoints
as a multivariate
problem.
It is
shown
how this latter model can provide a summary of the
quality
of
life
response
conditional on patient survival that with further work should
have
great application to
such quality of
life data.
Finally, the problem of intermittent missing
data is
reviewed.
The implications
of missing
data for some of the analyses presented
in the thesis
are
assessed,
and two
models
that
attempt
to determine the nature of intermittent missing
data
are
developed. It is
concluded that the
problem of non-ignorable intermittent missing
data
presents
a very
challenging
area of
further
research
