Evaluation
Trials commonly include both outcome measures and process measures.
Outcome measures indicate whether or not the intervention has worked.
For example, the intended outcome of a deprescribing trial may be an
improvement in quality of life or reduction in hospital readmissions.
The outcome measure is used to inform the required sample size of a
trial. The larger the difference that is anticipated between
intervention and control, the smaller the required sample size to detect
this difference with adequate precision. Researchers may choose a
non-inferiority study; however, in most cases the required sample size
prohibits such studies. In order to prove that the outcome from the
intervention is ‘no worse’ than the control, the study requires a sample
size that can detect a difference that is deemed negligible in clinical
significance. This means that the study has to be powered to detect a
very small difference unlike superiority studies when the research has
to prove that the intervention is better than the control and choose a
sample size accordingly.
Process measures represent intermediate steps to achieving an outcome.
An example of a process measure to help explain the observed outcome is
the number of medicines stopped. For example, in a trial powered to
detect a difference in quality of life, if intervention participants
have a significantly higher quality of life than control participants
then we might assume that the intervention is effective. If, however, we
find that the average number of medicines stopped is the same in both
intervention and control arms, then we know that something other than
stopping medicines is attributable to the difference.
Given that deprescribing is a complex behaviour with multiple
determinants, effective interventions will also be complex as they need
to comprise multiple components to address the target determinants. For
example, the practitioner behaviour change intervention being tested in
the CompreHensive geriAtRician-led
MEdication Review (CHARMER) trial, aims to address five determinants of
proactive deprescribing in hospital and thus comprises five intervention
components. It is essential to evaluate the intervention’s mechanism of
action in order to understand how the intervention leads to any changes
in deprescribing activities and how this then impacts on the primary
outcome measure. Understanding mechanisms of actions can be achieved by
developing questionnaires or surveys (6). To understand how the CHARMER
deprescribing intervention changes the behaviours of pharmacists and
geriatricians, we developed a questionnaire to identify changes in the
five target determinants. This will enable identification of any
targeted determinants that the intervention does not address and help
explain how the intervention works (or does not work) in changing
proactive deprescribing.
The role of condition-orientated measures in evaluating trials is
unclear. For some research funders, condition-orientated measures such
as blood pressure or lipid profile are acceptable trial outcome measures
whilst for other research funders, these are deemed process outcomes.
This disparity in expectations generates variation in practice and
therefore reduces opportunity for the results from deprescribing trials
to be compared, and trial data to be aggregated to increase the power
and thus precision of the estimated effect of deprescribing. A Core
Outcome Set (COS) is an agreed, standardised set of outcomes to be
measured and reported in all clinical trials of a particular health
condition or specific area of healthcare. Research teams can measureadditional outcomes in their deprescribing trials, in addition to
the COS, to suit their context and particular focus. A COS for hospital
deprescribing trials for older people under the care of a geriatrician
was recently reported (10). Consistent use of this COS by deprescribing
trials will enhance uniformity of reporting; there is also a need for
funders to align expectations on acceptable outcome measures.