Evidence forms the basis of modern medicine. Clinical research provides us with this evidence, guiding health professionals towards solutions to problems that they face in daily practice. Transferring existing problems in medical practice to a research setting is a challenging process that requires careful consideration. The practice of clinical epidemiology aims to address this through the application of established approaches for research in human populations, while at all times focussing on the problem at hand from a clinical perspective. This course teaches the principles and practice of clinical epidemiology, drawing on real problems faced by medical professionals and elaborating on existing examples of clinical research. Medical researchers will lean how to translate real clinical problems into tangible research questions for investigation, gaining insight into some of the most important considerations when designing an epidemiological study along the way. Core concepts will be introduced along four key themes: diagnosis, prognosis, treatment and etiology. Followers of this course will develop their understanding of the topics addressed through lectures from experts, peer interaction and review assignments.
Unintended Effects
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En provenance du cours de Utrecht University
Clinical Epidemiology
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Intervention Research
In our final week of lectures, you will step into the world of intervention research, exploring the "Therapeutic" branch of the DEPTh model. We will focus on the study of intended and unintended effects, exploring a new kind of study design: the randomized controlled trial.
Rencontrer les enseignants
Diederick E. GrobbeeProfessor
Clinical Epidemiology
Arno W. HoesProfessor
Clinical Epidemiology
Up until now,
we have looked at how we can study the intended effects of an intervention.
Focusing on how to obtain valid and
generalizable estimates of the advocacy of an intervention.
But research into any unintended adverse effects that
may result from the use of an intervention is essential for clinicians so
that they can weigh up the pros and cons of the intervention for their patient.
As with research into the intended effects of an intervention,
research into the unintended effects is also causal in nature.
If a patient's health declines after receiving a certain intervention,
it's essential that clinicians know whether the adverse effects were
caused by the intervention itself,
as this may lead the clinicians to discontinue treatment for a patient.
Furthermore, if a therapy is known to cause severe side effects,
a clinician may decide not to begin with that intervention and
instead, may seek an alternative intervention.
Adverse drug reactions can have a range of presentations, and
it's helpful to identify which kinds of effects we want to investigate,
as our choice will determine the kind of study that we will need to conduct.
A simple yet effective approach is to use the Type A and B classification system.
Originally proposed by Rawlins and Thompson in the 1970s.
Type A side effects are characterized as being relatively common,
and to an extent predictable.
Generally, Type A side effects are dose dependent, and as the intervention dosage
is increased, the severity of the Type A side effects will gradually worsen.
Type B side effects, on the other hand,
are much rarer than Type A side effects, and it can be
very difficult to predict when an adverse reaction of this type will occur.
The side effects are also generally more severe and can often be life threatening.
One example of such a Type B side effect is an allergic reaction
to a drug leading to anaphylactic shock, which, if left untreated, could be fatal.
So how can we go about studying the unintended effects of an intervention?
In an ideal situation, we would study both Type A and
type B side effects within a single study.
But as you can imagine, often this is not possible because of
differences in the frequency of occurrence, and
different approaches are needed to address these research problems.
Let's start with Type A side effects.
Earlier, we began planning an intervention study to
assess the efficacy of a new polypill in patients with a moderate
risk of developing cardiovascular disease within the next ten years.
While the results of early studies suggested that the drug is safe in humans,
more information is needed about the frequency of common side effects that it
may cause including, dizziness, cough, and gastric bleeding,
all potentially directly relate to the primary actions of the drugs.
And importantly, we will want evidence that can establish whether
any of these conditions are directly caused by taking the polypill.
Once again, in order to address this question,
we take an epidemiological approach.
We begin by defining the study determinant as the polypill,
which we can compare against the placebo pill.
And multiple outcomes to assess different types of Type A side effects.
As with research into the intended effects of an intervention,
to obtain valid findings in our study, we will need to ensure comparability
at the level of the intervention to level of outcome, and patient characteristics.
And, as you've probably already guessed,
a randomized control trial would be an excellent tool to help us achieve this.
It could even be a good idea to combine this study with our original study
looking at the efficacy of the polypill,
in which case we would treat the Type A side effects as secondary outcomes.
Randomization assures comparability of prognosis which, in a non run most study,
could be different between treated and untreated patients.
Because physicians may be reluctant to give the pill to those patients who they
expect would be particularly vulnerable to, for example, gastric bleeding.
This would be the case for patients who require the use of an NSAID for
other reasons.
So once again, an RCT is the design of choice for
intervention research in Type A, unintended effects.
However, there are some circumstances in which an RCT
is not the best type of study to assess adverse effects, nor even essential for
valid comparisons, and this brings us to research in to Type B side effects.
After a few years in the market clinicians begin to notice that a small minority of
patients who were prescribed the polypill developed aseptic meningitis.
Concern has been raised that aseptic meningitis may be a rare
allergic Type B side effect of the drug possibly resulting from some form
of interaction of the separate components in a single pill.
We could consider setting up a post marketing cohort study to address this.
But we need to question whether prognosis is comparable for
those treated and untreated, as discussed previously, for intended effects.
A particular feature, however, of Type B effects is that they cannot be easily
predicted for the individual patient, and thus are not likely to have played a role
in setting the indication for use in patients.
Consequently, unlike Type A effects that can be explained by the primary actions
of the drug, patients who are or are not given the drug may well be comparable for
the prognosis of aseptic meningitis.
Even patients without an indication at all, and therefore not receiving the drug
would be comparable for the risk of this particular outcome.
While theoretically,
an RCT may give us the best chance of obtaining findings that are free of bias,
if the side effect occurs only in say, 1 in every 10 or 100,000 patients,
we need an extremely large trial to be able to reach any firm conclusions.
And a study of that scale simply would not be feasible.
And given a lack of confounding by indication for
this outcome, an observational study may provide an equally valid comparison.
Yet, due to the infrequency of the outcome, still a very large number of
patients may be needed to be followed up over a period of several years.
An alternative, more efficient approach could be to use a case control design
in which patients who develop the outcome are first identified
along with control patients from the general population.
Next, their prescription histories could then be checked.
The validity of the findings in a cohort or case control study would
given the comparability of prognosis, not differ, and the same results in either
approach will be obtained after a far smaller investment of time and money.
So we have seen that addressing the clinical problem of
unintended effects can be challenging, especially when the side effects are rare.
The approach that you take to investigate unintended effects will depend greatly on
the type of outcome that you're interested in studying.
And on a final note,
while the categorization of unintended effects into Types A and B can be useful
when defining an intervention research question, there are some situations
where unintended effects do not fall neatly into one of these categories.
For example, it may be that the side effect has dose dependent properties, and
therefore is predictable in principle but it is still uncommon.
In such cases care must be taken to choose an appropriate
study design that takes into account any potential sources of bias.
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