randomised trials masoud solaymani-dodaran iran university of medical sciences
TRANSCRIPT
Randomised Trials
Masoud Solaymani-Dodaran
Iran University of Medical Sciences
How do we know a treatment works?
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
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
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
Scurvy killed thousands seaman each year
Lind learned of sailor recovering from scurvy on a diet of grasses
47 year wasted
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
Or no treatment at
all
What do we expect if new
treatment works?
Selection of subjects
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?
Why shouldn’t we just give the new treatment to people and see if it works?
Subject allocation: Studies without a control group1
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
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
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
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
Story of sea captain with anti-nausea pills
Predictability of assignment system, role of the investigator
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
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
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
RandomisedNot Randomised
Stratified randomisation
Why? Because in small numbers the groups might not still be comparable
Data collection: Outcomes
Primary and secondary Desired effects, side effects Robust and standardises methods of
measurements
Data collections: Prognostic profile at entry
Baseline information To check comparability of the groups
Masking (Blinding) 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
Difference in the two groups are important not just the shear amount
Cross over design
Factorial design
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)
Overt: people stop participating Covert: stopping without admitting Tests can be done e.g. urine test for
metabolites
Drop-ins
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 non-compliance
Trial of treatment of hypertension Pilot study was done to separate non-
compliers The problem may be lack for generalisability
The net effect of non-compliance
Reducing observed differences Underestimation Example of clofibrate and placebo to reduce
cholestrol
Are compliers and non-compliers different?
Sample size
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
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
αP-Value
β
Power
Factors you need to calculate sample size
1. The difference in response rate to be detected
2. An estimate of the response rate in one of the groups
3. Level of significance (Alpha error)
4. Power (Beta error)
5. Whether the test should be one-sided or two sided
What are one sided and two sided tests?
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
Example one: Cure rate=10% expecting 5% improvement α=0.05 and β=0.20
Example two: Cure rate=50% expecting 30% improvement α=0.05 and β=0.20
Formula for mean
n=[(Z1-α/2 + Zβ)2 S2] / d2
mean: 371standard deviation: 222α = 0.05 z=1.96β = 0.20 Power = 0.80 z=0.84d = Expected differencen=[(Z1-α/2 + Zβ)2 S2] / d2
The number needed in each arm:An increase of 15% means an increase of about 56N=[(1.96+0.84)2(222)2]/(56)2=125Total = 250
Number needed to Treat NNT
Number of patients who would need to be treated to prevent one adverse outcome such as death
Number needed to Harm NNH
Can be calculated for adverse effects The same as NNT
Internal and external Validity
Whether the study is well done and findings are valid
Are basic concerns in conduct of any trial
Three major US Randomised Trials
HDFP Hypertension Detection and Follow-up program (1)
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)
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
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)
MRFITThe Multiple Risk Factor Intervention Trial (1)
Aim: To determine whether mortality from myocardial infarction could be reduced by changes in lifestyle and other measures
MRFITThe Multiple Risk Factor Intervention Trial (2)
MRFITThe Multiple Risk Factor Intervention Trial (3)
MRFITThe Multiple Risk Factor Intervention Trial (4)
Breast cancer prevention using Tamoxifen (1)
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
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
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)
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)
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)