objective value-judgements, namel ya judgement of what is a good outcome. It is
generall yargued that clinical trials are designed to find out certain effects of a
drug, for example the lowering of plasma cholesterol levels; these effects are
capable of being measured b ya piece of laborator yequipment. The findings that
this equipment produces will be independent of the experimenters' perceptions
and hence can be said to be objective. This point is accepted. However, in what
follows I argue that the significance given to the effect and whether that effect is to
be termed a good outcome, are not factors inherent in the data but the values we
impose on the data.
Randomised control trials ,RCTs) are designed to produce data on the effec-
tiveness of a treatment. These trials can be organised in two ways. First, by com-
paring the new treatment with a placebo and second b ycomparing it with an
existing treatment. The clinical trial, that seeks to provide information on the
comparison between a new treatment and/or a placebo and an existing treatment,
is a practical technique to enable clinicians to make working comparisons between
different treatments. These trials are often called intention-to-treat trials as the yare
designed to establish the clinical effect of the drug or treatment.
The purpose of intention-to-treat trials is to assess whether a drug works not
how it works. The yprovide information on what treatment isbetterthan another
or moreeffectivethan a placebo. It is in this assessment of what makes a treatment
better than another that trials incorporate evaluative elements. The researcher
makes a value judgement as to whether a particular effect is good or bad and hence
whether the treatment is effective. Effectiveness, good outcomes, quality, a `better'
treatment are not pre-existing facts waiting to be discovered b ymedical science:
the yare value-laden assessments of the weight given to a particular effect of the
treatment. Thus, to sa ya treatment is effective is summing up one's opinion on the
data.
For example, a clinical trial ma yproduce data that sa ythat treatment X has a
48% success rate in treating a given condition. Such data do not automaticall ytell
us whether this treatment is an effective treatment for our given condition and
whether we should recommend it to our patients. Our assessment of how good the
48% success rate is cannot be objectivel ydetermined, but is dependent on a
number of factors. First, the severit yof the condition being treated. If a condition is
life threatening a 48% chance of success would be ver ygood and the treatment
would be judged to be ver yeffective. Second, the acceptable level of side-effects of
this treatment will depend on the type of condition that is treated. If the condition
is life threatening we will bear ver ybad side-effects to achieve this 48% success rate
,i.e. the side-effects of chemotherap yare ver ysevere but held to be acceptable).
However, for a minor complaint we would not see such side-effects as acceptable
and would not class the treatment as an effective one. Third, the existence of other
treatments and how the new treatment compares will influence how effective we
judge our treatment to be. If there is another treatment Y with a 60% success rate
and comparable side-effects our treatment will not be seen as effective. If treatment
Yhasmuch worse side-effects than our treatment X, determining which treatment
is most effective will be a matter of individual clinical judgement and will depend
on the goals and preferences of the standard setting authority.
It could be said that it is not important that the results produced b yclinical trials
Clinical Governance 243