Donepezil, rivastigmine, galantamine and memantine for ...

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Economic analysis
Neumann and colleagues 179 use the Consortium to
Establish a Registry for Alzheimer’s Disease
(CERAD) database to examine the progression of
disease to mild, moderate and severe stages of AD,
with death included at all stages of AD
progression. CERAD is a longitudinal database of
1145 dementia patients examined annually
between 1986 and 1995. 180 Stage of severity of AD
(mild, moderate and severe) is based on the CDR,
a measure of cognitive function. Data analysis is
undertaken using a Cox proportional hazards
model, to estimate transition probabilities for
stage-to-stage and stage-to-nursing home
transitions, presenting annual rates. The transition
probabilities estimated by Neumann and
colleagues underscore the rapid and progressive
nature of AD. They show as an example a cohort
of mild community-based patients with AD, where
disease progresses to the severe/nursing home
stage of AD in 25.3% of patients, and results in
death in 25.5% of patients over 5-years.
As expected, there was a decrease in the numbers
in the CERAD cohort over time, with 1145 patients
assessed on entry, 774 at year 1, dropping to 367
in year 4 and to 85 and 16 patients assessed in
years 7 and 8, respectively. However, the mortality
rates in the stage transition matrix appear low,
with annual probabilities for death in mild,
moderate and severe, at 2.1, 5.3 and 15.3%,
respectively. Compared with UK all-cause
mortality rates, the mild and moderate death rates
would appear an underestimate of the general
population mortality rates, regardless of the
presence of AD or not. However, the CERAD
cohort is reported as having an age distribution
that is different (younger) than the expected UK
AD treatment population. In CERAD 45% are
aged ≥ 75 years, whereas in the AD2000 trial 43 in
the UK over 80% of patients are aged >75 years.
The CERAD data showed a varied course of
disease progression in AD, with age, gender and
behavioural symptoms shown to have an impact
on transition probabilities, supporting a view that
modelling disease progression around cognitive
function is a suboptimal approach. The authors
also highlight concerns over the sample sizes
involved in modelling disease progression over
time, commenting that the small numbers made
adjusting time-dependent probabilities for age
and gender difficult, and that the CERAD data
did not account for the time during which patients
may have had symptoms before entry to CERAD.
The CERAD stage transition matrix (in various
presentations) may be a helpful tool to model
baseline disease progression in a cohort of AD
patients, but issues over generalisability of the
patient group and adjustment to transit
probabilities when patients are subject to drug
therapy (e.g. donepezil, rivastigmine or
galantamine) are issues that would require
attention in any application of the data.
Mendiondo and colleagues present an approach
for the modelling of AD progression over time. 128
They use data from CERAD to model change in
MMSE as a function of time in the CERAD
population. The model uses MMSE alone to
predict disease progression over time, with the
authors arguing that the different symptoms of
AD, including daily function, cognitive
impairment, or global impression of severity or
change, all reflect the same underlying
pathological process. However, the present authors
believe that the current literature highlights that
cognitive function alone is not a good predictor of
AD progression. 43,122–125,178 Mendiondo and
colleagues use data from CERAD with MMSE
scores from 719 patients followed between
6 months and 7 years (mean 2.3 years), with values
of MMSE between 24 and 3 used in the statistical
modelling of disease progression (MMSE) relative
to time. Mendiondo and colleagues present a
mathematical representation of decline in MMSE
over time, with decline dependent on average
MMSE score between time intervals examined.
They also present findings to show that age is a
significant factor in AD progression; education was
seen to be a marginally significant factor. The data
suggested that disease progression is more rapid
when it affects younger individuals and, given the
effect of education on disease progression, the
authors speculate that it may be a result of a better
initial performance on MMSE by those regarded as
better educated (delaying diagnosis and making
the course of disease apparently more rapid).
Mendiondo and colleagues warn that there was
considerable heterogeneity in the raw
observational data used in the modelling of disease
progression, with data showing variability in
measurement of MMSE unrelated to disease
progression, with environmental and patient
factors also offering a potential to influence
estimates of disease progression.
The approach presented by Caro and colleagues 131
offers an opportunity to consider disease
progression across a broader description of AD
than cognitive function, using patients’
characteristics and other non-cognitive AD
variables. The approach of Caro and colleagues
(the AHEAD model) is described in some detail
above, and it is adapted in an illustrative model
below, yet it still remains limited given the crude