Adherence means sticking to the treatment you
are allocated. For example, if you are asked to take tablets as
part of a trial, it's about taking the right number of tablets at
the right time, and, if appropriate, finishing the course.
Adverse events are undesired effects that may or may not be
related to a treatment. For example, if you are given a drug to
treat an illness and you become sick (e.g. dizziness, stomach ache
or a rash), this would be described as an adverse event. If your
sickness is caused by the drug, this would be called a side effect.
Clinical trials will often look at both short- and long-term
adverse events related to a treatment. Some adverse events may be
serious and need to be reported to regulatory authorities (usually
the MHRA or FDA).
In research, the term ‘bias’ is used when a
particular design or analysis is likely to favour a particular
outcome. In a clinical trial, if one treatment is always given to
participants who have a more severe form of a disease, then this
treatment will appear worse than others. Bias can also happen if a
researcher knows about the treatment a participant is receiving,
and this interferes with the researcher’s ability to be
impartial.
It is important to avoid bias in health research, as it can
distort the results and could lead to unsafe or ineffective
treatments being licensed for use, or useful treatments being
overlooked. Researchers try to avoid bias by using randomisation and
by ‘blinding’
those assessing the results of treatments, which may be both the
patient and the doctor.
Blinding means that whoever is receiving or assessing the
effects of treatment does not know which treatment the person has
received. This helps to prevent bias. Sometimes
the participant will assess the effects of treatment, sometimes the
researcher will, and sometimes a researcher who is independent of
the trial will carry out this assessment.
In a double blind trial, neither the
participant, the doctor nor the researchers running the trial will
know which treatment the participant is receiving. The aim is to
avoid the hopes and expectations about the treatment, as well as
the hopes and expectations of the researchers, influencing the way
the benefits and risks are assessed.
It is not always possible to avoid researchers, doctors
and participants knowing which treatment they are having. For
example, the trial may be comparing surgery with no surgery. If the
researcher knows which treatment a participant is receiving, it may
be necessary for an independent researcher, who has not been
involved in conducting the trial, to assess the impact of the
different treatments.
Clinical trials are research studies involving
participants, that compare a new or different type of treatment
with the best treatment currently available. They test whether the
new or different treatment is safe and effective by comparing it to
what already exists. No matter how promising a new treatment may
appear during tests in a laboratory, it must go through clinical
trials before its benefits and risks can really be known. This also
applies to many different forms of treatment, such as surgery,
radiotherapy, physical and behavioural interventions, not just
drugs. If there is no standard treatment, the new treatment is
usually compared with no treatment or with a ‘dummy’ treatment (or
placebo).
If you take part in a crossover trial, your
treatment will change partway through the trial. For example, if a
trial is comparing the effectiveness of 2 different sorts of
exercise, you might take part in exercise A for the first part of
the trial and then exercise B for the second, then perhaps back to
A again – and so on. You cross over from one
treatment group to the other, and comparisons are then made between
how well you felt during the different periods. Often there will be
several cross-overs in a crossover trial.
Most trials have an independent data
monitoring committee that follows the progress of the trial and
makes sure it is being run properly. The people on the data
monitoring committee are experts in clinical trials, statistics or
in the disease being studied. They are independent of the
researchers running the trial. If they think that participants are
experiencing serious or unexpected side
effects, or if evidence has emerged that one of the treatments
being compared is clearly better than the others, they can advise
that a trial is stopped.
All trials have guidelines about who can
take part. These are called ‘eligibility criteria’, consisting
of inclusion
criteria and exclusion
criteria. For example, the eligibility criteria for a trial
looking at bi-polar disorder might say that the only people who can
take part are people who are over 18 but under 80, and who have
bi-polar disorder, but no other health problems.
An epidemiological study looks at how certain
exposures (for example, an exposure may be secondary smoke or
unprotected sex) or ‘risk factors’ affect health outcomes. An
epidemiological study in HIV/AIDS might ask:
- How common is HIV in a particular part of the world?
- Who has HIV? (For example, do more women than men have
HIV? What age are the people who have HIV?
- How did they get HIV? (For example, was it through
unprotected sex? Or from their mother when she gave
birth?)
An evidence base is a collection of the best
available scientific research currently available about a
healthcare topic, such as how well a treatment or a service works.
This evidence is used by health and social care professionals to
make decisions about the services that they provide and what care
or treatment to offer people who use services.
Exclusion criteria determine who is not
able to join a trial – for example, many trials exclude women who
are pregnant, or who may become pregnant, to avoid any possible
danger to a baby. Trials may also exclude people who are taking a
drug that interacts with the treatment being studied. (See
also eligibility
criteria and inclusion
criteria.)
In some clinical trials, it can be important
to compare how much different treatments or treatment plans cost,
as well as how well they work. This can be particularly important
when two (or more) treatments are equally effective, but where one
costs much more than the other.
The gathering and analysis of information
about costs is called health economics. Health economic evaluation
gives researchers, policymakers and those who deliver care a way to
think about health benefits and costs. This enables them to try to
get the best health gain for the most people, within a limited
budget.
For example, economic costs involved in
treating cancer include the cost of treatment, care and recovery,
as well as the costs of prevention and training of healthcare
personnel. Other costs include the economic costs of illness and
premature death, the loss of economic productivity, decreases in
the productivity of family members, and welfare and health
insurance expenditure.
Inclusion criteria determine who can join
a trial. For example, some trials only include people of a certain
age, or at a particular stage in their illness. You may have to
have a medical examination before a trial to assess whether you are
suitable to take part. (See also eligibility
criteria and exclusion
criteria.)
Individual participant data meta-analysis
is a specific type of meta-analysis where the researchers go back
and look at the records for each participant who took part in every
trial, instead of using summary information about groups of
participants (records are anonymised so the researchers don’t know
participants’ names). Then they bring these individual results
together. This makes the results of the meta-analysis more reliable
and enables researchers to look at how treatments have worked in
different groups of participants, e.g. age group or sex.
You cannot be entered into a trial without
signing a form saying that you have given your informed consent,
except in extreme circumstances (for example, if you're admitted to
hospital in an emergency and you're unconscious). If you sign this
form, you are saying that you believe you have been given all the
important facts about a trial, you understand them and that you
have decided to take part in the trial of your own free will. Even
after giving your informed consent, you are free to withdraw from
the trial at any time without giving a reason and without it
affecting your healthcare.
Within the context of healthcare, an
intervention is something that is given to a participant as a
treatment. For example, giving a drug is an intervention.
Counselling and surgery are also interventions.
Within the context of a clinical trial, the
‘intervention arm’ is the name given to the group of people
receiving the new treatment or treatment plan.
A meta-analysis involves a researcher bringing
together the numerical results of all previous research (usually
randomised trials) about one particular treatment or plan.
A meta-analysis can be important because it
allows us to pick up small differences between treatments. These
differences can be very hard to spot, so trials need to include
large numbers of participants to pick these up. Many trials are not
big enough, so we cannot be sure whether any differences that we
find are because of real differences between the treatments or just
due to chance. By bringing together the results of all trials of a
particular treatment in a meta-analysis, we can look at the
experience of more participants than in a single trial. This gives
a more reliable and accurate measurement of the effect of the
treatment and the best way of seeing which treatments are best.
Methodological research in relation to clinical
trials is research looking at ways to improve the how trials are
designed, carried out, conducted, analysed, interpreted and
reported.
In an observational or epidemiological study,
researchers do not offer different treatments as part of the
research. They study how certain ‘risk factors’ and disease
outcomes are related.
The term ‘open trial’ may refer to:
- A trial that is still recruiting people or
following them up. When a trial is closed, it stops recruiting
people and following them up. The researchers collect and analyse
the results, ready for publication
- A trial where the researcher and the
participant know which treatment they are receiving – they are not
blinded (see blinding).
This is usually called an ‘open label’ trial
In an open label trial, both you and your
doctor will know which treatment you are receiving. This is
the opposite of a double-blind
trial (see blinding).
Outcomes are changes in a participant’s health
state. For example an outcome might be that your blood pressure is
reduced as a result of taking tablets prescribed by the doctor.
Outcome measures are used to measure the effects of a treatment.
They might include physical measurements - for example measuring
blood pressure, or psychological measurements - for example
measuring people’s sense of well-being. If someone takes part in
research, they may be asked questions, or may be asked to have
extra tests to assess how well the treatment or service has
worked.
A placebo is a dummy treatment that is
designed to be harmless and to have no effect. It looks, smells and
tastes like the treatment being tested, so that people don’t know
if they are taking the dummy treatment or the treatment being
tested (see blinding). It
allows researchers to test whether a new intervention
has any benefit other than a psychological response, where
people feel better because they have received a treatment. This
response is called the 'placebo effect'. By comparing people’s
responses to the placebo and to the treatment being tested,
researchers can tell whether the treatment is having any real
benefit.
A protocol is the plan for a piece of
research. All protocols for clinical trials need to be approved by
an ethics committee (link). A protocol usually includes information
about:
- What question the research is asking and its
importance/relevance
- The background and context of the research,
including what other research has been done before
- How many people will be involved
- Who can take part
- The research method
- What will happen to the results and how they
will be publicised
A protocol describes in great detail
what the researchers will do during the research. Usually, it
cannot be changed without going back to a research ethics committee
for approval..
As well as measuring the physical effects of a
treatment (for example changes to blood pressure), many trials now
try to assess the impact of treatments on people’s quality of life.
For example, a ‘quality of life’ study might ask about:
- Your mood and general sense of
well-being
- Whether you feel more tired than usual
- Whether you are managing to do more things
than before
- Whether your sleep patterns have
changed
If you take part in a randomised controlled
trial, you will have an equal chance of receiving any of the
treatments being compared. The decision about which treatment
you’ll receive is based on chance. A computer will decide which
treatment you’ll receive, not you or the doctor. This is called
randomisation.
Randomisation ensures that the groups of
people receiving different treatments in a trial are as similar as
possible, except for the treatment they receive. This is important
because it means that researchers can be sure that any differences
between the groups are only due to the treatment.
Randomisation is the best way of ensuring that the results
of trials are not biased. For example, if a doctor chose which
treatment a participant should receive as part of a trial, she or
he might give the new treatment to sicker participants, or to
younger participants. This would make the results of a trial
unreliable. Randomisation helps prevent this kind of
bias.
Many clinical
trials are randomised controlled trials (RCTs). Clinical trials
aim to make a fair comparison between a new treatment and the
current treatment on offer, or between two (or more) existing
treatments, to see which one works best.
A controlled trial compares
two groups of people: an experimental group who receive the new
treatment, and a control group who receive the usual treatment or a
placebo. The control group allows the researchers to see whether
the treatment they are testing is any more or less effective than
the usual or standard treatment.
If you take part in
a randomised controlled trial, you will have an
equal chance of receiving any of the treatments being compared. The
decision about which treatment you’ll receive is random – or based
on chance. A computer will decide which treatment you’ll receive,
not you or the doctor. This is called randomisation.
Randomisation ensures that the two groups of
people in a trial are as similar as possible, except for the
treatment they receive. This is important because it means that
researchers can be sure that any differences between the groups are
only due to the treatment.
Randomisation is also the best way of ensuring
that the results of trials are not biased. For
example, if a doctor chose which treatment a participant should
receive as part of a trial, she or he might give the new treatment
to sicker participants, or to younger participants. This would make
the results of a trial unreliable. Randomisation helps prevent this
kind of bias.
Side effects are undesired effects that are
related to a treatment. For example, if you are given a drug to
treat an illness and it makes you sick (e.g. dizziness, stomach
ache or a rash), this would be described as a side effect. Clinical
trials will often look at short- and long-term side effects related
to a treatment. Some side effects may be serious and need to be
reported to regulatory authorities (usually the MHRA or FDA).
Systematic reviews aim to bring together the
results of all studies that have been carried out around the world
addressing a particular research question. They provide a
comprehensive and unbiased summary of the research.
For example, one clinical trial may not
give a clear answer about the effectiveness of a treatment. This
might be because the difference between the treatments being tested
was very small, or because only a small number of people took part
in the trial. So systematic reviews are used to bring the results
of a number of similar trials together, to piece together and
assess the quality of all of the evidence. Combining the results
from a number of trials may give a clearer picture. When
researchers combine the numerical results of these trials and
compare them, this is called a meta-analysis.
Last Update Date : 8/3/2011
