A systematic review of the effectiveness of adalimumab

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Health economics
TABLE 42 Fitting beta distribution to HAQ change data for
leflunomide
square, the larger the number of simulated
patients with that pair of HAQ values. It can be
seen that there is a high proportion of patients
with equal HAQ on treatment compared with
before treatment. In this example, the sampled
population contains a large number of zero initial
HAQ values. These are omitted from the graphs,
but included in the calculations relating to HAQ
improvement.
Table 43 shows the parameters found for the
beta distributions. Two sets of figures are given for
each of the TNF inhibitors: one for early RA
HAQ on treatment
3
2
1
0
Mean SD
Initial HAQ parameters 1.01 0.66
Initial HAQ sampled 1.03 0.62
HAQ improvement 0.48 0.50
a b
Beta parameters 0.57 0.65
and one for late RA. The columns headed
a and b are the actual parameters of the
distribution, while the column headed Mean
gives the mean value of the distribution. Since
the distribution is for a multiplier giving HAQ
improvement, the higher the mean, the more
effective the treatment. Consider, for example, a
patient with HAQ before treatment equal to 2.5.
The effect of a treatment with mean 0.6 will lie
somewhere between two extremes. One extreme
is that all patients have HAQ reduced by
0.6 × 2.5 = 1.5, so that HAQ on treatment would
be 1.0, while the other extreme is that 60% of
patients have HAQ reduced to zero, while the
other 40% have no change in HAQ. Where
values of a and b are both less than 1, as is
generally the case for the values used here,
the distribution is close to the second of
these cases.
Time on treatments
The model allows for two stages of early quitting
of treatment. Figure 48 shows the general shape
for the survival curve assumed for a particular
treatment. The first step represents cessation of
treatment after 6 weeks, which is assumed to be
0 1 2 3
HAQ before treatment
FIGURE 47 Modelled distribution of HAQ change on starting leflunomide