thesis
Non-ignorable missing covariate data in parametric survival analysis
- Publication date
- Publisher
Abstract
Within
any epidemiological study missing
data is
almost inevitable.
This
missing
data is often ignored; however,
unless we can assume quite
restrictive mechanisms, this will
lead to biased estimates. Our
motivation
are data collected to study the long-term
effect of severity of disability
upon
survival in children with cerebral palsy (henceforth CP). The analysis of
such an old
data set brings to light
statistical difficulties. The main issue in
this data is the amount of missing covariate data. We raise concerns about
the mechanism causing data to be missing.
We present a flexible
class of joint models for the survival times and the
missing
data mechanism which allows us to vary the mechanism causing
the missing
data. Simulation studies prove this model to be both
precise
and reliable in estimating survival with missing data. We show that long
term survival in the moderately
disabled is high and, therefore, a large
proportion will
be
surviving to times when they require care specifically
for
elderly CP sufferers.
In
particular, our models suggest that survival
from diagnosis is considerably higher than has been previously estimated
from this data.
This thesis contributes to the discussion of possible methods for dealing
with
NMAR data