1carl-fredrik burman, 11 nov 2008 rss / mrc / nihr hta futility meeting futility stopping...
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1 Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting
Futility stopping
Carl-Fredrik Burman, PhD
Statistical Science Director
AstraZeneca R&D
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting
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Stakeholder perspectives
The patient
A pharmaceutical company
The public (MRC, NIHR)
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting
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The fundamental design requirement:Ethics
”My old mother – principle”The trial is ethical if (and only if)I would recommend my mother
to take part in the trial,given that she would be eligible.
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting
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Interim stopping
Stop the trial as soon as I would not include my mother, e.g. if
One (publicly available) treatment is clearly better
A “new” treatment fails to show sufficient effect, when it has known safety disadvantages
No ethical obligation to stop If two treatments with similar safety have no clear difference in
effect
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting
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(Genuine) informed consent
The patient should get Full information regarding the trial treatments (and procedures),
including previous data, potential risks, etc.
Help to understand the information and
Apply it to his/her specific situation (health status, preferences)
When would a fully informed, fully competent patient give consent?
If and only if it is better (not worse) for him/her to take part in the trial, as compared to receiving standard therapy.
Cf. “my old mother” principle
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting
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Easy-going clinical equipose is not enough Clinical equipose
If there is uncertainty about which treatment is better
(Alternatively, compelling evidence of one treatment being better)
(Alternatively, medical experts disagree)
It’s far too easy to say that we are uncertain
I expect my doctor to say what he believes is best
7 RSS / MRC / NIHR HTA Futility Meeting
Carl-Fredrik Burman, 11 Nov 2008
Our old Mother
Scientific equipose Not every expert agree on
CO2-induced global warming
Do you suggest a randomised N-of-1 trial?
Of course not — choose the treatment we believe is best
Earth
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting
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What is ”best” for the patient?May depend on e.g. Effect (best guess + uncertainty)
Safety
Better care in the trial?
Economic compensation (but beware of exploitation)
Altruism
Likely effect will differ between individuals (covariates)
Preferences are different
Decision theory may help decide (at least in theory …)
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting
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Decision analysis (DA)
Patient perspective
Utility function U(effect, safety, QoL, cost, …)
Model for effect, safety, etc., based on best information (data, expert knowledge, …). Often Bayesian prior.
Choose decision (volunteer to participate in trial, or not) to maximise expected utility
The DA approach can also be used by a trial sponsor
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting10
A pharmaceutical company perspective(simplified) A new drug will be licensed if and only if the (next) phase
III trial has a statistically significant effect (p<5%) If licensed, the company will make a profit of V (unit: £) The trial cost is k·N, where N is the sample size The assumed (believed) treatment effect is . Maximise
V · Power(N) – k · N
Of course, this model is wrong (as all models are).
Should e.g. have V=V(T)=V(T(N)), where T is time.
11 RSS / MRC / NIHR HTA Futility Meeting
Carl-Fredrik Burman, 11 Nov 2008
0
20
40
60
80
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0 500 1000 1500 2000
Number of patients
mUSD
Gain
Net gain = Gain – Cost
Cost
Optimal sample size
Nopt = 1010
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting12
The interim decision(continue vs. stop for futility) Value V if significant
Conditional power CP if trial is continued
C additional trial cost if continued (compared to if stopped)
Continue iff V · CP > C,
that is, iff CP > C / V
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting13
DA vs. ”least clinically relevant” effect
DA approach: Maximise expected utility based on ”best guess” effect (or prior)
Traditional approach: 90% power at ”least clinically relevant” effect
What is the least clinically relevant effect? If no adverse effects, no cost And the outcome is death
One single saved life is clinically relevant … at least to the one saved
What is a relevant effect depends on safety, cost etc.
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting14
Conditional power at interim
Final estimate is N(, 1/N).
Stage i has sample size Ni and estimate i. Then = (N1·1+N2·2)/N
Statistical significance if > C / N (where C=1.96 say)
CP = P( > C / N ) = ( ·N2+ 1·N1/N2-C (N/N2) )
But which to use when calculating CP? Original alternative Alternative ?
Interim estimate 1 ?
Linear combination of 1 and Alternative ?
Bayesian posterior based on interim data ?
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting15
Stop, continue, or something else?
Run a new trial?
Sample size reestimation, based on interim estimates Flexible design methodology (Bauer & Köhne –94)
Predefined weights for the different stages (generally, weight not proportional to information)
May change the sample size for stage 2 after viewing interim results
Discussion on CP
Somewhat controversial
May be better than design with only futility stopping
Group-sequential designs should often be preferred
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting16
Publicly funded trial:Treatments with similar safety Assume
Whole patient population will receive one of these treatments
Efficacy is the only unknown
Same safety, cost, etc.
The closer the interim effect is to zero, the more value in continuing
Thus, no reason to stop for futility
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting17
Example 1: Value of information
Compare 2 treatments with probabilities pA, pB for death.
Assume total future population size is T (10,000 say)
If we knew that , we would choose treatment A T·lives would be spared as compared to using B
Similarily, choose B if <0
Net value T·Abs()
or T·Abs()/2 if compared to using random treatment
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting18
Maximal value of information
Before trial, p2p1 has approximately normal prior with mean=0, SD= (say 10%)
What would the value be if we could learn the exact value of ?
Take the Bayesian expectation of the value T·Abs()/2,
Eprior [T·Abs()/2] = T· / (2)
With T=10,000 and =10%, about 400 lives would be spared
Example cont’d
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting19
0
50
100
150
200
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2Prior mean
Sa
ved
live
s
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting20
Publicly funded trial:Intervention vs. no treatment (placebo) Assume
Intervention is associated with some cost, safety risks
Not clear whether intervention has a positive effect
If effect, then the size of the effect will determine the size of the patient population which will get a positive net benefit
First objective: is there any effect?
Reasonable to stop for futility if interim estimate is low
Expected value by continuing study is then small
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting21
Information leakage
In regulatory setting, large discussion on who should see interim data
Does the DMC have to be independent from the sponsor
What are the risks of potential information leakage?
Problems may be over-emphasised?
The ethical aspect
Carl-Fredrik Burman, 11 Nov 2008RSS / MRC / NIHR HTA Futility Meeting22
Summary
Futility stopping may be an ethical requirement
Industry funded trials: Tradeoff cost and expected value
Publicly funded trials (examples) Don’t stop for futility if two active treatments differ only in effect
May stop for futility if “active” treatment unlikely to have sufficient effect (tradeoff cost and value)
(If basic science objective …)