Randomised Trials
Masoud Solaymani-Dodaran
Iran University of Medical Sciences
How do we know a treatment
works?
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All who drink of this treatment recover in a
short time, except those whom it does not
help, who all die, it is obvious, therefore, that
it fails only in incurable cases"
Galen (129-c. 199) cited from “Epidemiology” by Gordis
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The randomized trial is considered the ideal
design for evaluating both the effectiveness
and the side effects of new forms of
intervention.
An unplanned trial 1510-1590
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Ambroise Pare, the surgeon (1510-1590)
Boiling oil finished
He used a mixture of Yolk of egg, oil of rose,
and turpentine
The day after the results were amazing
He decided to never cauterize again
A planned trial, James Lind
1747
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Scurvy killed thousands seaman each
year
Lind learned of sailor recovering from
scurvy on a diet of grasses
47 year wasted
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His explanation of dietary cause for scurvy
was not acceped
It took 47 years for British Admiralty to let him
repeat the experiment
On entire fleet of ships
Dramatic results
1795: lemon juice standard part of british
seaman’s diet (limeys)
General design of a randomised
trial
What do we
expect if new
treatment works?
Or no
treatment at
all
Selection of subjects
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Written and clear criteria
The test is to give the same result not matter
who applies the criteria
No room for subjective variability
Easier said than done
Question?
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Why shouldn’t we just give the new treatment
to people and see if it works?
Subject allocation: Studies
without a control group1
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The story of great Boston surgeon
Vascular reconstruction on a large number of
patients
“Did I not operate on half of my patients?”
That would have doomed half of them to their
death
Coincidence
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The story of the man in the bathtub
The question is if we administer a drug and
patient gets improved; Is one the cause of the
other?
“Results can always be improved by omitting
controls”

Professor Hugo Muensch of Harvard University
Historical controls
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we go back to the records of patients with the
same disease who were treated before the
new therapy became available
Problems with historical
control
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Gathering data with different intentions,
quality of data collection
Many things other than therapy will change
over calendar time (living conditions,
nutrition, life style, etc)
Simultaneous nonrandomised
controls
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Story of sea captain with anti-nausea pills
Predictability of assignment
system, role of the investigator
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Trial of anticoagulant therapy after world war
II
Even and odd days for receiving and not
receiving intervention
BCG Vaccination for tuberculosis,
role of subjects
Subjected decided who wants to be vaccinated
Subjected were allocated in an alternative fashion
Randomization
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Randomisation in effect means tossing a coin
to decide the assignment of a patient
Table of random numbers
Can we guess the sequence
Practical

You have been asked to determine patient
allocation for a study which tests two new
forms of drugs for treatment of psoriasis.
Using random table randomise 30 patients to
two intervention and one control group.
Conflict with experience!
What do we achieve by
randomization
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Equal chances for any subject to enter either
the treatment or control group
Comparable groups
Balanced distribution of confounders even
for confounders that we don’t know
Practical: describe what you
see
Not Randomised
Randomised
Stratified randomisation
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Why? Because in small numbers the groups might
not still be comparable
Data collection: Outcomes
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Primary and secondary
Desired effects, side effects
Robust and standardises methods of
measurements
Data collections: Prognostic
profile at entry
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Baseline information
To check comparability of the groups
Masking (Blinding)
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Why we should mask?
Enthusiasm, certain psychological factors
Trial of Vit C in common cold
Comparing those thought to have received placebo and those
thought to have received treatment
Side effects in those receiving
placebo
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Difference in the two groups are important not just
the shear amount
Cross over design
Factorial design
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Testing two drugs
Modes of actions are independent
Factorial design
Factorial design, example of
Aspirin and Beta-carotene study
•The aspirin part of study was terminated, because of
obvious results in 44% reduction of myocardial infarction
•Beta-carotene continued for 12 years and showed no
effect in reducing cancer or heart disease
Non-compliance (dropouts)
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Overt: people stop participating
Covert: stopping without admitting
Tests can be done e.g. urine test for
metabolites
Drop-ins
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Aspirin and Beta-carotene trial
Buying aspirin over the counter
Controls were provided with a list of drugs
they should avoid
Urine test for salicylates was done
What can be done to avoid noncompliance
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Trial of treatment of hypertension
Pilot study was done to separate noncompliers
The problem may be lack for generalisability
The net effect of noncompliance
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Reducing observed differences
Underestimation
Example of clofibrate and placebo to reduce
cholestrol
Are compliers and non-compliers
different?
Sample size
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How many subjects do we have to study?
Comparing two populations
Two Jar of beads each containing 100 beads
Whether distribution of the beads by colour differs in jars A and B?
Can we conclude the two
population are different?
Can we conclude the two
population are the same?
From sample to the whole
population
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When we study we only compare samples
But we generalise our conclusion to the
whole population
Therefore there are always the possibility of
errors
Four possibilities in testing
whether treatment differ (1)
Four possibilities in testing
whether treatment differ (2)
Four possibilities in testing
whether treatment differ (3)
What does P<0.5 mean?
Power
Summary of terms
α
P-Value
β
Power
Factors you need to calculate
sample size
1.
2.
3.
4.
5.
The difference in response rate to be
detected
An estimate of the response rate in one of
the groups
Level of significance (Alpha error)
Power (Beta error)
Whether the test should be one-sided or two
sided
What are one sided and two sided
tests?
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Example
Our present cure rate is 40%
The new treatment is expected to increase to 60%
The difference is 20%
Are you sure that is what you are going to find? If
yes you can use one sided test
If not you better test in both directions (two sided
tests)
Number of patients needed in
each group
α=0.05 and β=0.20 (two sided)
Number of patients needed in
each group
α=0.05 and β=0.20 (one sided)
Practical
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Example one:
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Cure rate=10%
expecting 5% improvement
α=0.05 and β=0.20
Example two:
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Cure rate=50%
expecting 30% improvement
α=0.05 and β=0.20
Formula for mean
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n=[(Z1-α/2 + Zβ)2 S2] / d2
mean: 371
standard deviation: 222
α = 0.05
z=1.96
β = 0.20
Power = 0.80 z=0.84
d = Expected difference
n=[(Z1-α/2 + Zβ)2 S2] / d2
The number needed in each arm:
An increase of 15% means an increase of about 56
N=[(1.96+0.84)2(222)2]/(56)2=125
Total = 250
Number needed to Treat NNT
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Number of patients who would need to be
treated to prevent one adverse outcome such
as death
Number needed to Harm NNH
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Can be calculated for adverse effects
The same as NNT
Internal and external Validity
Are basic concerns in conduct of any trial
Whether the study is well
done and findings are valid
Three major US
Randomised Trials
HDFP
Hypertension Detection and Follow-up
program (1)
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Question: Value of hypertension treatment in people with mild
to moderate hypertension (diastolic BP of 90-104
HDFP
Hypertension Detection and Follow-up
program (2)
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Stepped care: treatment according to a precisely defined
protocol, under which treatment was changed when a specified
decease in blood pressure had not been obtained during a certain
period
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Referred care group: referred back to their own
physicians
HDFP
Hypertension Detection and Follow-up
program (3)
Cumulative all cause mortality by blood pressure
status and type of care received
HDFP
Hypertension Detection and Follow-up
program (4)
MRFIT
The Multiple Risk Factor Intervention
Trial (1)
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Aim: To determine whether mortality from myocardial
infarction could be reduced by changes in lifestyle and other
measures
MRFIT
The Multiple Risk Factor Intervention
Trial (2)
MRFIT
The Multiple Risk Factor Intervention
Trial (3)
MRFIT
The Multiple Risk Factor Intervention
Trial (4)
Breast cancer prevention using
Tamoxifen (1)
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Tamoxifen reduces rate of cancer in the other
breast
Trial started in 1992
In 1997 there were 13388 women 35 and over
had been enrolled
20 mg daily tamoxifen for 5 years
In march 1998 independent data monitoring
committee decided to stop trial because of
sufficient evidence for reduction of invasive
and non-invasive breast cancer
Breast cancer prevention using
Tamoxifen (2)
Breast cancer prevention using
Tamoxifen (3)
•The potential benefits of tamoxifen must be weighed against the
increased incidence of endometrial cancer
•Two similar European studies did not find the reduction reported in
America
Phases in testing new drugs
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Phase I: clinical pharmacologic studies, small
studies of 20-80 look at toxic and pharmacologic
effects
Phase II: clinical investigation of 100-200 patients
for efficacy and relative safety
Phase III: large scale randomised controlled trials for
effectiveness and relative safety; often multi-centre
Phase IV: post marketing surveillance for possible
late adverse effects such as carcinogenesis and
teratogenesis
Ethical consideration
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Is randomization ethical?
At what point we “know” that drug A is better
than drug B?
Is it ethical not to randomize?
Whether truly informed consent can be
obtained?
Under what circumstances a trial should be
start earlier than planned? (DSMB)
RCT for evaluating Widely
Accepted Interventions
Trial of Arthroscopic Knee surgery
for Osteoarthritis (1)
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6% of adults over 30 and 12% of adults 0ver
65 have significant knee pain as a result of
osteoarthritis
A number of RCTs had shown more pain relief in those
operated compared to controls with no treatment
Trial of Arthroscopic Knee surgery
for Osteoarthritis (2)
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July 2002
Used sham arthroscopy
Assessors of pain were blinded
Patients themselves were blinded
Followed for 2 years
Trial of Arthroscopic Knee surgery
for Osteoarthritis (3)
Trial of Arthroscopic Knee surgery
for Osteoarthritis (4)
Trial of Arthroscopic Knee surgery
for Osteoarthritis (5)
Effect of Group Psychosocial support on
Survival of patients with Metastatic Breast
cancer (1)
Effect of Group Psychosocial support on
Survival of patients with Metastatic Breast
cancer (2)