Instructor : Fabrizio D’Ascenzo fabrizio.dascenzo@gmail emounito metcardio

Instructor: Fabrizio D’[email protected]

www.emounito.orgwww.metcardio.org

Role MD

RANDOMIZED CONTROLLED TRIAL

CONFLICT OF INTEREST

None

AIM OF THE COURSE

A critical appraisal

- Theorical- Practical

of RCT

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)

RANDOMIZED

It prevents selection bias and insures against accidental bias.

It produces comparable groups, and eliminates the source of bias in treatment assignments.

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

„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

CRUCIAL CONCEPTSPHASESTRUCTURESUPERIORITY AND INFERIORITYRANDOMIZATIONBLINDING

SAMPLE SIZEAD INTERIM ANALYSISITT VS ATSUBGROUP ANALYSIS

PHASE

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

STRUCTURE

STRUCTURE

Parallel group

Cluster randomized

Crossover

Factorial

PARALLEL

Most randomized controlled trials have

parallel designs in

which each group of participants is exposed to

only one of the

study interventions.

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). 

CLUSTER RANDOMIZED

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

CROSSOVER

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

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

FACTORIAL DESIGN

two or more experimental interventions are not only

evaluated separately but also in combination and against a

control

FACTORIAL DESIGN

It allows evaluation of the interaction that

may exist between two treatments.

FACTORIAL DESIGN

two or more experimental interventions are not only

evaluated separately but also in combination and against a

control

SUPERIORITY AND INFERIORITY

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

SUPERIORITY

A properly designed and conducted superiority

trial,

is entirely interpretable without further

assumptions

(other than lack of bias or poor study conduct)

SUPERIORITY

The difference between the new and active

control treatment is enough to support the

conclusion that the new test drug is also

effective

INFERIORITY

INFERIORITY LIMIT

M 1 = the largest clinically acceptable

difference (degree of inferiority) of the test

drug compared to the active control

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

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

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

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

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.

RANDOMIZATION

RANDOMIZATION

1- To conceal

2- To generate

TO CONCEAL

Allocation concealment prevents investigators

from influencing which participants are

assigned to a given intervention group

>

Increasing risk of selection bias

Evidence shows that reports of trials reporting inadequate allocation concealment are associated with exaggerated treatment

effects

TO GENERATE

Use of computer or random number table

http://www.randomization.com/

Balanced randomisation involves selecting

certain baseline covariates (called

balancing variables) and incorporating them

into the randomisation scheme in a way

TO GENERATE

SIMPLE (UNRESTRICTED) RANDOMISATION

No other allocation generation approach, irrespective of its

complexity and sophistication, surpasses the unpredictability

and bias prevention of simple randomisation.

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

RESTRICTED RANDOMISATION

It controls the

probability of obtaining an allocation sequence

with an

undesirable sample size imbalance in the

intervention

groups

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.

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

LIMITS OF RANDOMIZATION

Balanced randomisation introduces

correlation between

treatment groups, which violates the

statistical assumption that

all patients are independent

Balanced (simple)

randomisation forces

the outcomes between

treatment arms to

be similar

(apart from

any treatment effect)

Variables used in the randomisation process should

subsequently be adjusted for in the analysis?

LIMITS OF RANDOMIZATION

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.

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

BLINDING

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.

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)

BLINDING (MASKING)

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.

DOUBLE DUMMY

SAMPLE SIZE COMPUTATION

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.

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.

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

SAMPLE SIZE FOR NON INFERIORITY

For NI trial

A small sample size is needed

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)

AD INTERIM ANALYSIS

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).

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?

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

INTENTION TO TREAT

vs

AS TREATED

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

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

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.

Intention to treat analysis

As treated analysis

How can we decide on 5 events?

SUBGROUP ANALYSIS

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

.

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

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

The pre-specificed ones

that is according to stratified randomization

are the most reliables ones

SUBGROUP ANALYSIS

TAKE HOME MESSAGES

- Check how randomization is performed

- Check how blinding is performed

- Check about superiority and inferiority structure

THANKS A LOT!!!!