Instructor: Fabrizio D’Ascenzo [email protected] Role MD RANDOMIZED CONTROLLED TRIAL.
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Transcript of Instructor: Fabrizio D’Ascenzo [email protected] Role MD RANDOMIZED CONTROLLED TRIAL.
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Instructor: Fabrizio D’[email protected]
www.emounito.orgwww.metcardio.org
Role MD
RANDOMIZED CONTROLLED TRIAL
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CONFLICT OF INTEREST
None
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AIM OF THE COURSE
A critical appraisal
- Theorical- Practical
of RCT
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SOME HISTORY
- 600 B.C.:Daniel of Judah compared the health
effects of the vegetarian diet with those of a royal Babylonian diet
over a 10-day period. (Book of Daniel 1:1–21)
-1952 The Medical Research Council trials on streptomycin for
pulmonary tuberculosis are rightly regarded as a landmark that
ushered in a new era of medicine. (Hill AB. The clinical trial. N
Engl J Med 1952; 247:113–119)
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RANDOMIZED
It prevents selection bias and insures against accidental bias.
It produces comparable groups, and eliminates the source of bias in treatment assignments.
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It permits the use of probability theory to express the likelihood of chance as a
source for the difference between outcomes.
It facilitates blinding (masking) of the identity of
treatments from investigators, participants, and
assessors, including the possible use of a placebo
RANDOMIZATION
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„Produces groups that are not systematically different
with regard to known and unknown prognostic factors
„ Permits a valid analysis
Permutation test is justified by randomization
Standard analyses are valid approximations of the
correct permutation test
RANDOMIZATION
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CRUCIAL CONCEPTSPHASESTRUCTURESUPERIORITY AND INFERIORITYRANDOMIZATIONBLINDING
SAMPLE SIZEAD INTERIM ANALYSISITT VS ATSUBGROUP ANALYSIS
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PHASE
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Phase I trialsObjective to determine a safe drug doseDesign usually dose escalation/de-escalationSubjects healthy volunteers or patients with disease
Phase II trialsObjective to determine a safe drug doseDesign often single armSubjects patients with disease
Phase III trialsObjective to compare efficacy of the new treatment with the standard regimenDesign usually randomized controlSubjects patients with disease
PHASE
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STRUCTURE
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STRUCTURE
Parallel group
Cluster randomized
Crossover
Factorial
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PARALLEL
Most randomized controlled trials have
parallel designs in
which each group of participants is exposed to
only one of the
study interventions.
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CLUSTER RANDOMIZED
A cluster randomized trial is a trial in which
individuals are randomized in groups (i.e.
the group is randomized, not the
individual).
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CLUSTER RANDOMIZED
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This design, obviously, is appropriate only
for chronic conditions that are fairly stable
over time and for interventions that last
a short time within the patient and that do
not interfere with one another.
CROSSOVER
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CROSSOVER
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Removing patient variation in this way makes crossover
trials potentially more efficient than similar sized, parallel
group trials in which each subject is exposed to only one
treatment
In theory treatment effects can be estimated with greater
precision given the same number of subjects.
CROSSOVER
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The principal drawback of the crossover trial is that the effects
of one treatment may “carry over” and alter the response to
subsequent treatments.
The usual approach to preventing this is
to introduce a washout (no treatment) period between
consecutive treatments which is long enough to allow the
effects of a treatment to wear off.
CROSSOVER
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FACTORIAL DESIGN
two or more experimental interventions are not only
evaluated separately but also in combination and against a
control
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FACTORIAL DESIGN
It allows evaluation of the interaction that
may exist between two treatments.
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FACTORIAL DESIGN
two or more experimental interventions are not only
evaluated separately but also in combination and against a
control
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SUPERIORITY AND INFERIORITY
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SUPERIORITY AND INFERIORITY
• FDA’s regulations on adequate and well-controlled studies (21 CFR 314.126) describe four kinds of concurrently controlled trials that provide evidence of effectiveness.
• Three are superiority controlled trials: placebo no treatment dose-response controlled trials
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SUPERIORITY
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A properly designed and conducted superiority
trial,
is entirely interpretable without further
assumptions
(other than lack of bias or poor study conduct)
SUPERIORITY
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The difference between the new and active
control treatment is enough to support the
conclusion that the new test drug is also
effective
INFERIORITY
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INFERIORITY LIMIT
M 1 = the largest clinically acceptable
difference (degree of inferiority) of the test
drug compared to the active control
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The critical problem, and the major focus of this
guidance, is determining M 1 , which is not measured in
the NI study (there is no concurrent placebo group).
It must be estimated (really assumed) based on the
past performance of the active control and by
comparison of prior test conditions to the current test
environment
INFERIORITY LIMIT
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One approach is to specify the equivalence margin on the basis of a clinical notion of a minimally important effect.
BUT
clearly subjective
The equivalence margin is often chosen with reference to the effect of the active control in historical placebo-controlled trials.
INFERIORITY LIMIT
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Someones claims that a positive noninferiority trial
implies that the new treatment is superior to placebo.
However, this claim requires an assumption that the effect
of the active control in the current trial is similar to its
effect in the historical trials.
INFERIORITY LIMIT
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Differences with respect to design features or by an inconsistency in the effect of the active
controls among the historical placebo-controlled trials (beyond that expected by random chance)
>
is often based on the lower bound of a confidence interval for that effect(accounting for within-trial and
trial-to-trial variability)
INFERIORITY LIMIT
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LIMIT OF INFERIORITY
• Non-inferiority studies are not conservative in nature since
limits in the design and conduct of the study will tend to
bias the results towards a conclusion of similarity.
• Poor compliance with the study medication, poor
diagnostic criteria, excessive variability of
measurements, and biased end-point assessment.
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RANDOMIZATION
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RANDOMIZATION
1- To conceal
2- To generate
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TO CONCEAL
Allocation concealment prevents investigators
from influencing which participants are
assigned to a given intervention group
>
Increasing risk of selection bias
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Evidence shows that reports of trials reporting inadequate allocation concealment are associated with exaggerated treatment
effects
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TO GENERATE
Use of computer or random number table
http://www.randomization.com/
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Balanced randomisation involves selecting
certain baseline covariates (called
balancing variables) and incorporating them
into the randomisation scheme in a way
TO GENERATE
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SIMPLE (UNRESTRICTED) RANDOMISATION
No other allocation generation approach, irrespective of its
complexity and sophistication, surpasses the unpredictability
and bias prevention of simple randomisation.
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With small sample sizes, simple randomisation (one-to-one allocation ratio) can yield highly
disparate sample sizes in the groups by chance, although becoming negligible with trial
sizes greater than 200.
BUT
However, interim analyses with sample sizes of less than 200 might result in disparate group
sizes.
SIMPLE (UNRESTRICTED)RANDOMIZATION
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RESTRICTED RANDOMISATION
It controls the
probability of obtaining an allocation sequence
with an
undesirable sample size imbalance in the
intervention
groups
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BLOCKING METHODS
Blocks may be fixed or variable
If the block size is fixed, especially if small (six participants or less), the block size could be deciphered in a not double-blinded trial.
Longer block sizes—eg, ten or 20—rather than smaller block sizes—four or six—and
random variation of block sizes help preserve unpredictability.
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RANDOM ALLOCATION RULE
For example, for a total study size of 200, placing 100
group
A balls and 100 group B balls in a hat and drawing them
randomly without replacement symbolises the random
allocation rule.
It is usually reported as use of envelopes
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LIMITS OF RANDOMIZATION
Balanced randomisation introduces
correlation between
treatment groups, which violates the
statistical assumption that
all patients are independent
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Balanced (simple)
randomisation forces
the outcomes between
treatment arms to
be similar
(apart from
any treatment effect)
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Variables used in the randomisation process should
subsequently be adjusted for in the analysis?
LIMITS OF RANDOMIZATION
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STRATIFIED RANDOMIZATION For example, with 6 diabetics, there is 22%
chance of 5-1 or 6-0 split by block
randomization only.
Stratified randomization is the solution to
achieve balance within
subgroups: use block randomization
separately for diabetics and non-diabetics.
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The block size should be relative small to maintain balance in small
strata. Increased number of stratification variables or increased
number of levels within strata leads to fewer patients per stratum.
Subjects should have baseline measurements taken before
randomization.
Large clinical trials don’t use stratification. It is unlikely to get
imbalance in subject characteristics in a large randomized trial.
STRATIFIED RANDOMIZATIOn
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BLINDING
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BLINDING (MASKING)
Keeping the trial participants, care providers,
data collectors, and some times those
analysing the data, unaware of which
intervention is being administered to which
participant, so that they will not be
influenced by that knowledge.
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Do not use single, double…
But symply report who is blinded
1)Patients
2)Those assessing the outcome
3)Those administering the intervention
BLINDING (MASKING)
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BLINDING (MASKING)
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The success of blinding could be assessed early in the first days of the study if possible
before the evidence of efficacy
Subjects could be asked to guess treatment assignment, but they should be allowed to express uncertainty and answer ‘‘do not
know.’’
If subjects are asked to guess treatment assignment, subjects’ answers (for example placebo/treatment/Do not know) should be
reported for each group.
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DOUBLE DUMMY
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SAMPLE SIZE COMPUTATION
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SAMPLE SIZE COMPUTATION
The aim of an a priori sample size calculation
is mainly to determinate the number of
participants needed to detect a clinically
relevant treatment effect.
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Type 1 error and power are usually fixed at conventional
levels (5% for type I error, 80% or 90% for power).
Assumptions related to the control group are often pre-
specified on the basis of previously observed data or
published results, and the expected treatment effect is
expected to be hypothesised as a clinically meaningful
effect.
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HOW TO MINIMIZE SAMPLE SIZE
• Use Continuous Measurements Instead of
Categories
• Use More Precise Measurements
• Use Paired Measurements
• Expand the Minimum Expected Difference
• Use Unequal Group Sizes
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SAMPLE SIZE FOR NON INFERIORITY
For NI trial
A small sample size is needed
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To evaluate sample size for a new drug with 20% of failure compared to 25% in the standard group:
- 1461 for group for superiority trial
- 298 for group (limit of inferiority 5)
- 133 for group (limit of inferiority 10)
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AD INTERIM ANALYSIS
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AD INTERIM ANALYSIS
The interests of participants should be best served
if recruitment is closed as soon as a clear answer is
available
Vs
The interests of societyshould be best met if recruitment continues untilthere is a clear answer (such that the results aresufficiently conclusive to lead to changes in the
clinical management of future patients).
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INDEPENDENT DATAMONITORING COMMITTEE
Is the inclusion rate of patients acceptable and as expected?
Is there an unexpectedly high rate of severe or life-threatening
adverse events, which may indicate the premature closure of
the trial?
Is the outcome of the trial treatment comparable with that of
the previous experience upon which the specific trial is based?
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If the interim analysis demonstrated a statistical significant
differences between the trial treatments that exceed the
differences defined by the statistical guidelines of the
trial
then
this would warrant closure of the study.
INDEPENDENT DATAMONITORING COMMITTEE
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INTENTION TO TREAT
vs
AS TREATED
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INTENTION TO TREAT
• Use every subject who was randomized according to randomized treatment assignment.
• „ Ignore noncompliance, protocol deviations, withdrawal, and anything that happens after randomization
• The ITT analysis holds the randomization as of paramount importance
• Š Deviation from the original randomized groups can contaminate the treatment comparison
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WHY INCLUDE NONCOMPLIANT SUBJECTS IN ITT ANALYSIS?
„ Compliance or noncompliance occurs after randomization
„ Attempting to account for noncompliance by excluding
noncompliant subjects can bias the treatment evaluation
In clinical practice, some patients are not fully compliant
„ Compliant subjects usually have better outcomes than
noncompliant subjects, regardless of treatment
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AS PROTOCOL/AS TREATED
All participants are analyzed according to the treatment they actually received, regardless
of what treatment they were originally allocated.
While this may have some initial appeal, once again the effect of random allocation is compromised, making the interpretation of
the results difficult.
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Intention to treat analysis
As treated analysis
How can we decide on 5 events?
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SUBGROUP ANALYSIS
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SUBGROUP ANALYSIS
If many are performed, it becomes likely that one or more will
spuriously be statistically significant.
In fact, if the subjects in a trial randomized between treatment
groups A and B are partitioned into G mutually exclusive subgroups
and a statistical significance test at α=0.05 is conducted
within each subgroup, then even if there is no true effect,
the probability of at least one significant result is 1 – (1 – α )G
.
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For α 0.05 and G 5, this probability is 23 percent;
for α 0.05 and G 10, the probability is 40 percent
Subgroup analyses also produce misleading reversals of
effects, especially if the overall result is barely significant.
SUBGROUP ANALYSIS
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A commonly used method for adjusting is dividing the overall significance level by the total number of subgroup analyses, also called the Bonferroni method.
For example, in a study with a significance level of 0.05 and 10 subgroup analyses, the significance level for each subgroup analysis would be 0.005.
However, some statisticians state that significant results are rarely observed after adjustment with the Bonferroni method
SUBGROUP ANALYSIS
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The pre-specificed ones
that is according to stratified randomization
are the most reliables ones
SUBGROUP ANALYSIS
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TAKE HOME MESSAGES
- Check how randomization is performed
- Check how blinding is performed
- Check about superiority and inferiority structure
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THANKS A LOT!!!!