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The
nature of epidemiological study is that the result of any estimate of
exposure
is considered only to be just that, an estimate of the true figure in a
conceptual complete population. Assuming that a study population was
randomly
sampled from this conceptual whole population (be it selected either by
exposure or disease status) then it is possible to calculate a 95%
confidence
interval around this estimate giving a range in which the likely true
effect
within the population as a whole lies. If this range excludes unity
then
a "statistically significant" association is said to exist. The problem
with most studies in this field given small numbers, is that very
rarely
does the 95% confidence interval exclude unity. Indeed, for example, a
95% confidence interval of 0.2-2.8 would suggest that the study results
would be compatible either with the implanted group having a risk a
fifth
of that to almost 3 times as that of an unexposed group. Clearly, such
a result does not advance science, as it is neither possible to exclude
a protective or harmful effect. In general, given the data, numerical
value
of the risk obtained is the best estimate. Results at the extreme of
the
95% confidence interval are less likely given that data.

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The
importance of what has been mentioned above might be illustrated in
considering
the appropriate sample size that would be necessary to undertake a
study
to be absolutely certain an important effect had not been missed. Using
the cohort approach and assuming there is a desire to detect a 1.5-fold
increased risk in an implanted group in a disease such as scleroderma
where
there is a background population incidence of approximately 10 per
million
per year then it would be necessary to follow-up 80,000 women for 10
years
to have a sufficient power to detect an effect. Similarly, if a case
control
design was to be adopted and using the most efficient matching system
of
four non-diseased women to every women with connective tissue disease,
similar calculations can be undertaken. Based on current studies, it
seems
likely that a 1% exposure to silicone breast implants might be expected
in the control group. To detect an increased risk of 1.5 in the
implanted
group would require some 5000 cases of the disease under investigation.
As an indication of the magnitude of this problem, there are unlikely
to
be enough women with scleroderma who would have been eligible to have
been
implanted in the United Kingdom.
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