TYPES
OF CLINICAL TRIALS
Clinical trials can be
classified based on different types:
The first way of
classifying clinical trials is in the way the researchers behave.
In an observational study,
the investigators observe the subjects and measure their outcomes. The
researchers do not actively manage the experiment. This is also called a
natural experiment. An example is the Nurses' Health Study.
•
In an interventional study, the
investigators give the research subjects a particular medicine or other
intervention. (Usually they compare the treated subjects to subjects who
receive no treatment or standard treatment.) Then the researchers measure how
the subjects' health changes.
•
Second way of classifying trials is by
their purpose. The U.S. National Institutes of Health (NIH) organizes trials
into five (5) different types:
•
Prevention trials: look for better ways
to prevent disease in people who have never had the disease or to prevent a
disease from returning. These approaches may include medicines, vitamins,
vaccines, minerals, or lifestyle changes.
•
Screening trials: test the best way to
detect certain diseases or health conditions.
•
Diagnostic trials: conducted to find
better tests or procedures for diagnosing a particular disease or condition.
•
Treatment trials: test experimental
treatments, new combinations of drugs, or new approaches to surgery or
radiation therapy.
•
Quality of Life trials: explore ways to
improve comfort and the quality of life for individuals with a chronic illness
(e.g. Supportive Care trials).
•
Genetic trials :-Determine how one’s
genetic makeup can influence detection, diagnosis, prognosis, and treatment
§ Broaden
understanding of causes of cancer
§ Develop
targeted treatments based on the genetics of a tumor
•
Academic clinical trials: They share a
valuable component of the health care system; they benefit patients and help to
determine the safety and efficacy of new drugs and devices.
Typical areas of
academic clinical trials are the advancement and optimization of already
existing therapies. Thus academic clinical trials may for instance test how a
combination of registered drugs may improve treatment outcomes or they may
apply registered treatments in additional, less frequent indications. Such
research questions are not a primary focus of for-profit companies and thus
these trials are typically initiated by individual investigators or academic
research organizations.
There are many
different organizations which have an interest in academic clinical trials and
facilitate or take part in their conduct. These organizations include:
•
Hospitals, universities, researchers and
institutions that view trials as a source of income and prestige and receive
private, charitable and governmental funding.
•
Pharmaceutical or biotech companies who
view the development and commercialization of treatments as their business.
•
Regulators who wish to ensure treatments
are safe and work effectively.
•
Patients and patients' organizations and
associations who want faster access to advanced treatments.
Academic clinical
trials are run at academic sites, such as medical schools, academic hospitals
and universities and non-academic sites which may be managed by so-called Site
Management Organizations (SMOs). Site management organizations are for-profit
organizations which enlist and manage the physician practice sites that
actually recruit and follow patients enrolled in clinical trials. In some
cases, academic members participate in clinical trials act as members of SMOs.
A crossover trial also
referred to as a crossover study is a clinical trial in which patients are
given all of the medications to be studied or one medication and a placebo in
random order. These studies are generally done on patients with chronic
diseases to control their symptoms. The data are analyzed according to the
original intention to treat.
A crossover study has
the advantage over a simple double-blind study that the variability between
patients is minimized because each patient crossing over in effect serves as
their own control. One disadvantage is that long term effects cannot be tracked
with this approach. Another disadvantage is that curative therapies cannot be
tested after one another or before a placebo.
There are some
important issues with respect to the design of cross-over studies. Two in
particular may often crop up.
First is the issue of
order effects, because it is possible that the order in which treatments are
administered may affect the outcome. An example might be a drug with many
adverse effects given first, making patients taking a second, less harmful
medicine, more sensitive to any adverse effect.
Second is the issue of
carry-over between treatments. In practice carry-over can be and is often dealt
with by the use of a wash-out period between treatments or by making
observations sufficiently later after the start of a treatment period that any
carry-over effect is minimized.
A
randomized controlled trial (RCT):
Randomization is a
process for allocating subjects between the different trial interventions. Each
subject has the same chance of being allocated to any group, which ensures
similarity in characteristics between the arms. This minimizes the effect of
both known and unknown confounders, and thus has a distinct advantage over
observational studies in which statistical adjustments can only be made for
known confounders. Although randomization is designed to produce groups with
similar characteristics, there will always be small differences because of
chance variation. Randomization cannot produce identical groups.
RCT is a type of
scientific experiment most commonly used in testing healthcare services (such
as medicine or nursing) or health technologies (such as pharmaceuticals,
medical devices or surgery). According to Lachin (1998), 'RCTs are considered
the most reliable form of scientific evidence in healthcare because they
eliminate spurious causality and bias'. RCTs are mainly used in clinical
studies, but are also employed in other sectors such as judicial, educational
and social research. As their name suggests, RCTs involve the random allocation
of different interventions (or treatments) to subjects. This ensures that known
and unknown confounding factors are evenly distributed between treatment
groups.
Sellers of medicines
throughout the ages have had to convince their consumers that the medicine
works. As science has progressed, public expectations have risen, and
government health budgets have become ever tighter, pressure has grown for a
reliable system to do this. Moreover, the public's concern for the dangers of
medical interventions has spurred both legislators and administrators to
provide an evidential basis for licensing or paying for new procedures and
medications. In most modern health-care systems all new medicines and surgical
procedures therefore have to undergo trials before being approved.
Trials are used to
establish average efficacy of a treatment as well as learn about its most
frequently occurring side-effects. This is meant to address the following
concerns. First, effects of a treatment may be small and therefore undetectable
except when studied systematically on a large population. Second, biological
organisms (including humans) are complex and do not react to the same stimulus
in the same way, which makes inference from single clinical reports very
unreliable and generally unacceptable as scientific evidence. Third, some
conditions will spontaneously go into remission, with many extant reports of
miraculous cures for no discernible reason. Finally, it is well-known and has
been proven that the act of administering the treatment itself may have direct,
sometimes very powerful, psychological effects on the patient, which is known
as the placebo effect.
Randomized trials are
employed to test efficacy while avoiding these factors. Trials may be open,
blind or double-blind.
Blind
trials
The randomization
process minimizes the potential for bias but the benefit could be greater if
the trial intervention given to each subject is concealed. Subjects or researchers
may have expectations associated with a particular treatment and knowing which
was given can create bias. This can affect how people respond to treatment or
how the researcher manages or assesses the subject. In subjects, this bias is
specifically referred to as the placebo effect. Humans have a remarkable
psychological ability to affect their own health status. The effect of any of
these biases could result in subjects receiving the new intervention appearing
to do better than those on the control treatment, but the difference is not
really due to the action of the new treatment.
Single-blind
trial
In a single-blind
trial, the researcher knows the details of the treatment but the patient does
not. Because the patient does not know which treatment is being administered
(the new treatment or another treatment) there might be no placebo effect. In
practice, since the researcher knows, it is possible for him to treat the
patient differently or to subconsciously hint to the patient important
treatment-related details, thus influencing the outcome of the study.
Double-blind
trial
An experiment designed
to test the effect of a treatment or substance by using groups of experimental
and control subjects in which neither the subjects nor the investigators know
which treatment or substance is being administered to which group. In a
double-blind test of a new drug, the substance may be identified to the
investigators by only a code. The purpose of a double-blind study is to
eliminate the risk of prejudgment by the participants, which could distort the
results. A double-blind study may be augmented by a cross-over experiment, in
which experimental subjects unknowingly become control subjects, and vice
versa, at some point in the study.
Triple-blind
trial
Some randomized
controlled trials are considered triple-blinded, although the meaning of this
may vary according to the exact study design. The most common meaning is that
the subject, researcher and person administering the treatment (often a
pharmacist) are blinded to what is being given. Alternately, it may mean that
the patient, researcher and statistician are blinded. These additional
precautions are often in place with the more commonly accepted term
"double blind trials", and thus the term "triple-blinded"
is infrequently used. However, it connotes an additional layer of security to
prevent undue influence of study results by anyone directly involved with the
study.
Aspects
of control in clinical trials
Traditionally the
control in randomized controlled trials refers to studying a group of treated
patients’ not in isolation but in comparison to other groups of patients, the
control groups, who by not receiving the treatment under study, give
investigators important clues to the effectiveness of the treatment, its side
effects, and the parameters that modify these effects.
Other aspects of
control include having other members of the research team, who will typically
review the test to try to remove any factors which might skew the results. For
example, it is important to have a test group which is reasonably balanced for
ages and sexes of the subjects (unless this is a treatment which will never be
used on a particular sex or age group). Additionally, peer review and/or review
by government regulators can be seen as another source of control. These bodies
examine the trial results when they are presented for publication or when the
drug manufacturer applies for a license for the drug.
The importance of
having a control group cannot be overstated. Merely being told that one is
receiving a miraculous cure can be enough to cure a patient—even if the pill
contains nothing more than sugar. Additionally, the procedure itself can
produce ill effects. For example, in one study on rabbits where these subjects
were receiving daily injections of a drug, it was found that they were
developing cancer. If this was a result of the treatment, it would obviously be
unsuitable for testing in humans. Because this result was reflected equally
between the control and test groups, the source of the problem was investigated
and it was shown in this case that the administration of daily injections was
the cancer risk not the drug itself.
The analysis of the
trial results requires knowledge of medicine, epidemiology and in particular
statistics. The branch of statistics that deals specifically with biomedical
research is biostatistics. Pharmaceutical firms employ groups of
biostatisticians to try to make sense of the data. Likewise, regulators pay
keen attention to the appropriateness of statistical methods used to analyze
trial results.
Types
of control groups
•
Placebo concurrent control group
•
Dose-response concurrent control group
•
Active concurrent control group
•
No treatment concurrent control group
•
Historical control
Randomization
in clinical trials
There are two processes
involved in randomizing patients to different interventions. First is choosing
a randomization procedure to generate a random and unpredictable sequence of
allocations. This may be a simple random assignment of patients to any of the
groups at equal probabilities, or may be complex and adaptive. A second and
more practical issue is allocation concealment, which refers to the stringent
precautions taken to ensure that the group assignment of patients are not
revealed to the study investigators prior to definitively allocating them to
their respective groups.
Randomization
procedures
There are a couple of
statistical issues to consider in generating the randomization sequences.
•
Balance: since most statistical tests
are most powerful when the groups being compared have equal sizes, it is
desirable for the randomization procedure to generate similarly-sized groups.
•
Selection bias: depending on the amount
of structure in the randomization procedure, investigators may be able to infer
the next group assignment by guessing which of the groups has been assigned the
least up to that point. This breaks allocation concealment (see below) and can
lead to bias in the selection of patients for enrollement in the study.
•
Accidental bias: if important covariates
that are related to the outcome are ignored in the statistical analysis,
estimates arising from that analysis may be biased. The potential magnitude of
that bias, if any, will depend on the randomization procedure.
Complete
randomization
This is commonly used
in which, each patient is effectively and randomly assigned to any one of the
groups. It is simple and optimal in the sense of robustness against both
selection and accidental biases. However, its main drawback is the possibility
of imbalances between the groups. In practice, imbalance is only a concern for
small sample sizes (n < 200).
Permuted
block randomization
In this form of
restricted randomization, blocks of k patients are created such that balance is
enforced within each block. For instance, let E stand for experimental group
and C for control group, then a block of k = 4 patients may be assigned to one
of EECC, ECEC, ECCE, CEEC, CECE, and CCEE, with equal probabilities of 1/6
each. Note that there are equal numbers of patients assigned to the experiment
and the control group in each block.
Permuted block
randomization has several advantages. In addition to promoting group balance at
the end of the trial, it also promotes periodic balance in the sense that
sequential patients are distributed equally between groups. This is
particularly important because clinical trials enroll patients sequentially,
such that there may be systematic differences between patients entering at
different times during the study.
Unfortunately, by
enforcing within-block balance, permuted block randomization is particularly
susceptible to selection bias. That is, since toward the end of each block the
investigators know the group with the least assignment up to that point must be
assigned proportionally more of the remainder, predicting future group
assignment becomes progressively easier. The remedy for this bias is to blind
investigator from group assignments and from the randomization procedure
itself.
Strictly speaking,
permuted block randomization should be followed by statistical analysis that
takes the blocking into account. However, for small block sizes this may become
infeasible. In practice it is recommended that intra-block correlation be
examined as a part of the statistical analysis.
A special case of
permuted block randomization is random allocation, in which the entire sample
is treated as one block.
Urn
randomization Designs:
Covariate-adaptive
randomization
When there are a number
of variables that may influence the outcome of a trial (for example, patient
age, gender or previous treatments) it is desirable to ensure a balance across
each of these variables. This can be done with a separate list of randomization
blocks for each combination of values - although this is only feasible when the
number of lists is small compared to the total number of patients. When the
number of variables or possible values are large a statistical method known as
Minimization can be used to minimize the imbalance within each of the factors.
Outcome-adaptive
randomization
For a randomized trial
in human subjects to be ethical, the investigator must believe before the trial
begins that all treatments under consideration are equally desirable. At the
end of the trial, one treatment may be selected as superior if a statistically
significant difference was discovered. Between the beginning and end of the
trial is an ethical grey zone. As patients are treated, evidence may accumulate
that one treatment is superior, and yet patients are still randomized equally between
all treatments until the trial ends.
Outcome-adaptive
randomization is a variation on traditional randomization designed to address
the ethical issue raised above. Randomization probabilities are adjusted
continuously throughout the trial in response to the data. The probability of a
treatment being assigned increases as the probability of that treatment being
superior increases. The statistical advantages of randomization are retained,
while on average more patients are assigned to superior treatments.
Allocation
concealment
In practice, in taking
care of individual patients, clinical investigators often find it difficult to
maintain impartiality. Stories abound of investigators holding up sealed
envelopes to lights or ransacking offices to determine group assignments in
order to dictate the assignment of their next patient. This introduces
selection bias and confounders and distorts the results of the study. Breaking
allocation concealment in randomized controlled trials is much more problematic
because in principle the randomization should have minimized such biases.
Some standard methods
of ensuring allocation concealment include:
•
Sequentially-Numbered, Opaque, Sealed
Envelopes (SNOSE)
•
Sequentially-numbered containers
•
Pharmacy controlled
•
Central randomization
Great care for
allocation concealment must go into the clinical trial protocol and reported in
detail in the publication. Recent studies have found that not only do most
publications not report their concealment procedure most of the publications
that do not report also have unclear concealment procedures in the protocols.
Most studies start with a 'null hypothesis'
which is being tested (usually along the lines of 'our new treatment x cures as
many patients as existing treatment y') and an alternative hypothesis ('x cures
more patients than y'). The analysis at the end will give a statistical
likelihood, based on the facts of whether the null hypothesis can be safely
rejected (saying that the new treatment does, in fact, result in more cures).
Nevertheless this is only a statistical likelihood, so false negatives and false
positives are possible. These are generally set an acceptable level (e.g., 1%
chance that it was a false result). However, this risk is cumulative, so if 200
trials are done (often the case for contentious matters) about 2 will show
contrary results. There is a tendency for these two to be seized on by those
who need that proof for their point of view.
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