Trials
Adrian Boyle
Objectives
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Design
Measures of quality
How to analyse data from an RCT
How to appraise an RCT
New terms
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Explanatory vs pragmatic
Surrogate end point
Equipoise
Factorial
Cross-over
Intention to treat
Number needed to treat
Equipoise
There should be substantial uncertainty in
the clinician’s mind about which treatment
is better for the patient before the patient
is enrolled in a trial
Why randomise?
• To avoid / reduce selection bias
• To balance confounders across groups
Limitations of trials
• Internal validity high at expense of low
external validity
• Efficacy rather than effectiveness
• Irrelevant narrow questions
• Often a ‘Shot in the dark’
• Drug companies often want to compare
against placebo, not standard treatment
• Expensive and time consuming
More jargon
• Phase 1 Clinical pharmacology Drug
safety in volunteers
• Phase 2 Initial investigation of effect
Effectiveness
• Phase 3 Full scale evaluation Compared
to placebo or standard practice
• Phase 4 Post marketing surveillance
Trial design
• Efficacy Frontier of
effect under ideal
circumstances works
• Explanatory
Provide clues as to
how the intervention
• Effectiveness How
this intervention
works in ‘the real
world’
• Pragmatic Shows
how well the
intervention works
Basic trial design
Population
Randomisation
Exposure1
Exposure 2
Outcome
Outcome
Analysis of basic study design
• Relative risk
incidence of outcome in group 1
DIVIDED BY
incidence of outcome in group 2
Sounds dramatic and sexy
Analysis of basic study design
• Absolute risk reduction:
incidence of outcome in group 1
MINUS
incidence of outcome in group 2
• Less sexy and more useful.
• Ask the next drug rep.
• Enjoy
Analysis of basic study design
• Number needed to treat
– Inverse of the absolute risk reduction
Example
• A trial of drug A compared to drug B found
that 20 out the 50 people who received A
were alive at one year compared to 15 /50
who received drug B
• What is the relative risk?
• What is the absolute risk?
• What is the NNT
Example
• Relative risk
– 20/50 = 0.4
– 15/50 = 0.3
– 0.4/0.3 = 1.33
• Or this could be expressed as a 33%
increase in survival at one year
NNT
• Absolute risk reduction 0.4-0.3 = 0.1
• This could be expressed as a risk
reduction of 10% NNT 1/0.1 = 10
• That is, you need to treat 10 people to
save one life in one year
Multi-variate analysis
• Adjust for potential confounders and bias
• Usually with logistic regression expressed
as a odds ratio
Factorial trial design
Population
Randomisation
Exposure2
Exposure1 Placebo
Placebo
Exposure1
Placebo
Exposure2
Placebo
Outcome
Outcome
Outcome
Outcome
Cross over design
Population
Randomisation
Exposure1
Exposure2
Outcome
Outcome
Exposure2
Exposure1
Outcome
Outcome
Cluster
The unit of randomisation is a group of
individuals e.g. GP practices or hospitals
Easier implementation of a complex design
Large studies
Seriously complicated statistics
Randomisation
Many methods
– Block
– Stratification
– Weighted
Simple
The proof of the pudding is in the table 1
Sources of bias in a trial
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Selection
Performance
Losses to follow up
Detection
What bias is there here?
Population 200
Randomisation
Exposure 1
Exposure 2
100
100
15
Outcome
10
Outcome
12
Is this bias?
Population 2000
Randomisation
Exposure1
100
Outcome
10
Exposure 2
100
Outcome
50
What bias is there here?
Population 200
Randomisation
Exposure1
Exposure 2
100
100
Outcome
10
No
Outcome 60
Outcome
20
No
Outcome 80
Outcome measure
• Does the outcome mean anything to you?
• Beware surrogate outcomes
• Beware composite outcome especially if
industry funded
Appraising a trial
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Identify aims
Identify study design
Identify population, exposure and outcome
Consider the randomisation
Consider the blinding
Consider the measurement
What biases and confounding factors are there?
What is the result and what does it mean
Assessing strength / quality
• JADAD scoring
• http://www.naturalstandard.org/explanation_columns.html