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)