Donepezil, rivastigmine, galantamine and memantine for ...

community-based care costs) were calculated based
on data presented by Stewart and colleagues 82 of
community costs from a societal perspective,
where 23% of these costs are assumed (cited as
assumption by Stewart and colleagues) to fall on
the public sector, an estimate of £3231 per year is
used in the industry model (the model assumes
this home care cost is unrelated to MMSE/severity
scores in base-case analysis). The model uses £887
per year for rivastigmine costs and an estimate of
monitoring costs (outpatient and GP visits) of
£468 for base-case analysis (with variations in
sensitivity analysis). Patients dropping out from
treatment (during a 6-month model cycle) are
allocated a 3-month drug and monitoring cost.
In base-case analysis the discount rate for future
costs and benefits is 3.5% per year.
Results presented in the industry submission on
cost-effectiveness of rivastigmine show an
incremental cost of £2121 and an incremental
QALY gain of 0.0862 from rivastigmine treatment
over 5 years, reflecting a cost per QALY of £24,616.
The submission states that where probabilistic
sensitivity analysis has been undertaken, involving
all key uncertain parameters, there is a probability
of over 80% that rivastigmine is considered costeffective
where the NHS is prepared to pay
£30,000 per QALY. From the CEAC presented, this
probability would appear to fall well below 20%
where the NHS was only prepared to pay £20,000
per QALY. We discuss below some concerns over
the methods used for probabilistic analysis.
In addition to probabilistic sensitivity analysis, the
submission presents findings from one-way
sensitivity analysis on important model parameters,
and the authors report that results are insensitive
to most of the parameters examined. However,
there is no one-way analysis performed on the
assumptions surrounding the calculation of QALY
values. Where cost-effectiveness analysis assumes
that the only cost considerations are drug costs and
monitoring costs (at base-case assumptions) the
cost per QALY is £39,563. Given that the
monitoring cost for those treated with rivastigmine
is one of the variables that the authors highlight as
impacting on results (i.e. cost per QALY sensitive
to monitoring assumptions), it seems reasonable to
assume that multi-way sensitivity analysis should be
undertaken. Were the analysis to assume higher
cost scenarios for monitoring and that only drug
and monitoring costs are relevant for costeffectiveness,
the cost per QALY would be much
higher than £40,000 (indeed, much higher than
£40,000 per QALY if lower QALY values were also
assumed in the sensitivity analysis).
© Queen’s Printer and Controller of HMSO 2006. All rights reserved.
Health Technology Assessment 2006; Vol. 10: No. 1
Comments on industry submission for
rivastigmine
We have highlighted above some concerns over
the analysis presented in the industry submission
for rivastigmine (see Appendix 15 for an outline
appraisal of the model). Importantly, we are
concerned at the sole use of MMSE to describe AD
severity, to model disease progression and to
reflect QALY gains associated with disease
progression. As discussed in other earlier and later
parts of this report, we believe that the use of
cognitive function alone to characterise and model
disease progression in AD is suboptimal and is
likely to misrepresent disease progression. A
further related and important concern is the use
of data in the model on disease progression in the
rivastigmine treated cohort which is from an
observational study, an open-label study, which
may be subject to serious bias, and which does not
reflect an ITT analysis. Treatment benefit is
reflected using data from treatment responders
over time, therefore in later time periods it is only
those patients who continue to benefit, and who
may be highly motivated, who are used in
comparison with a non-rivastigmine treated
cohort. The methodology used to predict disease
progression over time in an indirect
control/comparator group is from Mendiondo and
colleagues, 128 and this is based on MMSE scores.
The methodology from Mendiondo and
colleagues is intuitively appealing, but it is not
transparent (to us) from the published paper, or
the submitted industry model. The observational
data used by Mendiondo and colleagues to model
disease progression are from the CEARAD
database, and reflects a US patient group that may
not be generalisable to the UK treatment-eligible
population. Furthermore, we have not identified
any other applications of this methodology as a
means of validation.
The industry model is presented as a probabilistic
model, yet we have concerns over some of the
methods used in the model. The analysis does not
cover all possible variables in the probabilistic
sensitivity analysis, and for a number of
parameters (i.e. probability of institutionalisation,
costs for home care, monitoring costs) the model
does not apply distributions around a mean
parameter value; instead the model uses a number
of possible mean values (from various
sources/calculations) and randomly selects one of
the possible mean values (see further comment
below). This reflects a random ‘choice’ (or options)
of input parameters, and reflects what would be
regarded as a discrete distribution, although the
choice of possible inputs may have no relation to
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