A systematic review and economic model of the effectiveness and ...

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Review of economic evaluations of ADHD drug interventions in children and adolescents
TABLE 75 Results of amendments to the cost–utility model submitted by Janssen-Cilag
Amendment Comparison Incremental cost
per QALY (£)
None – base case Concerta XL vs IR-MPH 4,992
Second-line combination therapy with DEX (1) Concerta XL vs IR-MPH 4,903
Including drug costs during titration (2) Concerta XL vs IR-MPH 7,954
Taking BT component out of cost of discontinuing treatment (3)
Including co-morbidity costs as additional to
Concerta XL vs IR-MPH 19,303
responder/non-responder costs (4) Concerta XL vs IR-MPH 61
All of the above (1-4) Concerta XL vs IR-MPH 9,253
Model structure
The model assumes the same response to
combination treatment (i.e. drug treatment
plus BT), regardless of the accompanying drug
therapy. The model allows the combination
treatment to include the drug to which the
patient has previously failed to respond, or
experienced adverse events with as first-line
therapy. A more realistic assumption would have
been to have combination treatment with DEX,
according to the assumption used for third-line
pharmacotherapy in the model. This amendment
forms option 1 for the re-analyses shown in
Table 75.
Errors in model
As mentioned earlier in the review, the annual cost
of each drug excludes the cost during the average
titration period [Confidential information
removed]. This appears to have been omitted in
error. Correcting for this error forms option 2 for
the re-analyses shown in Table 75. An alternative
method would be to calculate more precisely a
weighted average cost using the actual number of
days spent on each dosage, rather than the
average over all doses. The monthly cost of a
patient discontinuing all treatments is assumed to
be equal to that for a non-responder to BT, but in
the model the cost also includes the total cost of a
BT treatment programme divided by 12. The
reasons for this are unclear; such a patient would
not receive the BT programme, only the follow-up
consultations and tests. This extra monthly cost is
fairly high (£86), and the effect of its removal can
be seen in option 3 in Table 75. Another error
appears to be present in the way that the cost of
co-morbidity is included in the model. As Table 69
shows, the annual medical cost associated with comorbidities
for patients receiving medical
treatment is lower than that for responders and
non-responders. Hence the 50% of nonresponders
who are assumed to have co-morbid
conditions cost less than those without comorbidities.
It appears that the quoted cost of comorbidities
actually refers to the additional cost of
co-morbidities, on top of medical costs for
responders and non-responders. This forms
option 4 in Table 75.
The assumption that the utility for the first month
of any treatment is equal to [Confidential
information removed] seems rather arbitrary. An
alternative approach would have been to observe
the number of patients responding at 1 month for
each treatment, and assume that on average these
patients responded half way through the first
month.
When re-run, the results of the probabilistic
analysis proved to be fairly variable. This can be
overcome by increasing the number of simulations
from 1000 to, for example, 10,000.
In the probabilistic sensitivity analysis, the
proportions switching to each second-line therapy
were modelled independently. This allowed the
sum of those proportions to fall below or exceed
one in nearly all simulations. Clearly, it is not
appropriate in a decision-modelling context to
have proportions or probabilities not summing
to one when all possible paths have been
identified. Therefore, for the two transitions
possible following first-line BT, this error is
corrected by making one of those probabilities (for
example, the probability of switching to DEX)
equal to one minus the other. For the three
transitions possible following first-line
pharmacotherapy, this error is corrected by
dichotomising the three-way transition and
adjusting the estimated probabilities appropriately
using Bayes’ revision theorem. These adjustments
ensure that the number of patients in the model
remains constant and does not increase or
decrease.