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

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Economic model
TABLE 98 Response rates defined as score of 1 or 2 on CGI-S
Trial Treatment Responders (%) No. in group
Greenhill, 2002 59
Steele, 2004 90
Kelsey, 2004 63
Michelson, 2002 74
Weiss, 2004 94
treatment strategy. Whether it is possible to
identify such patients in practice is a challenge for
the clinician in charge.
Sensitivity to estimated response rates
The section ‘Clinical effectiveness’ (p. 103)
illustrated the numerous definitions of response
available in the trial data. In order to utilise
different definitions of response, it was necessary
to model a relationship between the alternative
definitions. This was achieved by extending the
base case MTC model. As a reminder, the base
case model assumes a binomial likelihood from
the available data points, r and n, and the
proportion of responders, p, is estimated in a
regression-like structure, with the logit of the
proportion of responders dependent on a studyspecific
baseline, , and a treatment effect, :
r i k ~ Bin(pi k , ni k )
logit(p i k ) = i + i k
To extend this model, let us assume that response
defined as a score of 1 or 2 on the CGI-I is
response 1, which is modelled as follows:
r1 i k ~ Bin(p1i k , n1i k )
logit(p1 i k ) = 1i + 1 i k
Response defined as a score of 1 or 2 on the CGI-
S is response 2, and response on this scale is
modelled to be conditional on the estimated
response rate on scale 1:
r2 i k ~ Bin(p2i k , n2i k )
logit(p2 i k ) = 2i + 2 i k
If both measures capture the same effect, 2 may
be random error. If the measures capture different
effects, the relationship between the measures is
estimated according to this model, and the
ER-MPH8 98 (63) 154
Placebo 41 (26) 156
ER-MPH12
IR-MPH
[Confidential information removed]
ATX 34 (27) 126
Placebo 3 (5) 60
ATX 24 (29) 84
Placebo 8 (10) 83
ATX
Placebo
[Confidential information removed]
correlation between the two is reflected. Because
we have trials that report response on more than
one measure, the relationship between the
different measures is estimated from the data.
Through this relationship, trials that report
response only on scale 2 can inform the estimate
of response on scale 1. By selecting which
definition of response is to be the baseline in the
model (scale 1), we can infer response rates on
that scale, strengthened by information about
response on different scales. The code for this
extended model is given in Appendix 11.
Using the framework described above, we could
bring in trials reporting response on the CGI-S, in
order to synthesise all clinician-rated response
data. Two of the trials reporting response on CGI-
I for the base case analysis also reported response
defined as a score of 1 or 2 on the CGI-S. Three
more trials reported response on CGI-S, but not
on CGI-I. The additional information provided by
these trials is shown in Table 98.
The three additional trials were all set in the USA
and used DSM-IV diagnostic criteria. They
recruited patients of varying age ranges (6–12,
6–16 and 8–12 years), and this again is not
reflected in the model. The output from 2
WinBUGS models, using CGI-I and CGI-S as
baseline, respectively, is shown in Table 99.
Where CGI-I is used as the baseline response
definition, the results are slightly higher than the
base case analysis and the uncertainty around
the estimated treatment effects is reduced. Using
the same information, but specifying CGI-S
as the baseline scale, the order of the treatment
effects remains stable, but the absolute effects are
lower. This reflects the lower absolute response
rates reported using CGI-S in comparison
with CGI-I.