a quantitative approach to clinical development
DESCRIPTION
A Quantitative Approach to Clinical Development. Carl-Fredrik Burman, PhD Statistical Science Director AstraZeneca R&D, Sweden. A new paradigm (?). How should we get there?. Alternative designs (adaptive, cross-over, “traditional”). Where are we?. To where do we want to go?. - PowerPoint PPT PresentationTRANSCRIPT
![Page 1: A Quantitative Approach to Clinical Development](https://reader033.vdocuments.us/reader033/viewer/2022061504/56814767550346895db4a444/html5/thumbnails/1.jpg)
A Quantitative Approach to Clinical Development
Carl-Fredrik Burman, PhDStatistical Science DirectorAstraZeneca R&D, Sweden
![Page 2: A Quantitative Approach to Clinical Development](https://reader033.vdocuments.us/reader033/viewer/2022061504/56814767550346895db4a444/html5/thumbnails/2.jpg)
A new paradigm (?)
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Whereare we?
To wheredo we want
to go?
How should we get there?
Modeling
Decision Analysis (DA) to optimize design,
based on model& preferences
Alternative designs(adaptive, cross-over, “traditional”)
Preferences
Simulations
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Study designdecisions
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How statisticians used to design trials — A caricature
Medic (M): “What sample size do we need?”
Statistician (S): “Could you tell me the least clinically relevant effect, , please?”
M: It’s 20.S: “… and the standard deviation?”M: “It was 100 in the last trial”S: “Then it’s simple. N=1053 gives 90% power.”M: “Oh, we cannot afford that. Say that =30 instead.S: “Then the required sample size is 469.M: Excellent
The medics have taken care ofpopulation, duration, variable, etc.
![Page 6: A Quantitative Approach to Clinical Development](https://reader033.vdocuments.us/reader033/viewer/2022061504/56814767550346895db4a444/html5/thumbnails/6.jpg)
Whereare we?
To wheredo we want
to go?
How should we get there?
Modeling
Decision Analysis (DA)to optimize design,
based on model& preferences
Alternative designs(adaptive, cross-over, “traditional”)
Preferences
Simulations
![Page 7: A Quantitative Approach to Clinical Development](https://reader033.vdocuments.us/reader033/viewer/2022061504/56814767550346895db4a444/html5/thumbnails/7.jpg)
Example of astudy design
decision
Thanks to Claes Ekman & Björn Bältsjö
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Background• Loosely based on experiences from
• AZD7009 project (atrial fibrillation)• Compound in early phase II
• Potential side effect X• New results for stopped competitor drug, say.• Competitor drug-induced AE rate about 10%• Placebo rate likely to be about 1%• Minor AEs, no ethical complications
• Should a specific safety trial be added before entering next phase?
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AE probabilities
• q = P( AE | placebo ) • p = Drug-induced rate of X
• p>0 will hit sales• no approval if p>5%
• P( AE | drug ) = 1–(1-p)(1-q) = q+p(1-q) q+p
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Will trial results be interpretable?
• “Standard” design• n=30 subjects get active treatment• m=30 receive placebo
• Say that the number of AEs found are• x=2 on active treatment• y=0 on placebo
• Far from statistically significant
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Single-arm trial
• Historical data exist for placebo group• Alternative trial with n=60, m=0
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Formulation of priors• Prior for drug-induced AE probability
• P(p=0.00) = 0.6 Excellent• P(p=0.03) = 0.3 2nd line treatment• P(p=0.10) = 0.1 Not a viable treatment
• Prior for placebo AE probability• P(q=0.01) = 0.9• P(q=0.05) = 0.1
• Independence in prior distribution• NB! Model is too simplistic for practical use,
but may have pedagogical value
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0%
20%
40%
60%
80%
100%Prior distribution
p=0.00
p=0.03
p=0.10
Single-arm safety trial n=60 pat’s; x=3 AEs
Posterior = Prior + Data
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0%
20%
40%
60%
80%
100%Prior distribution
p=0.00
p=0.03
p=0.10
0%
20%
40%
60%
80%
100%
1
Posterior if x=3, n=60
p=0.00
p=0.03
p=0.10
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0%
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100%
0%
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Before trial / Prior
After n=60 patients
x=0 x=1 x=2 etc
p=0.00
p=0.03
p=0.10
p=0.00
p=0.03
p=0.10
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0%
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60%
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100%Before trial / Prior
0%
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Ideal (infinite info)
0%
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100%
After n=60 patients
x=0 x=1 x=2 etc
0%
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40%
60%
80%
100%After n=20 patients
x=0 etc
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Economic assumptions
• (Expected Net Present) Value V(p) before dose-finding:
• V(p=0.00) = 1000• V(p=0.03) = 100• V(p=0.10) = 0
• Planned dose-finding trial cost K = 500
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Total value ofsuggested safety trial (n=60)
E[Value] = …x Probability Project value0 32.2% 4331 24.9% 2802 14.4% 163 9.2% -1694 6.1% -243… … …
• E[ Value | Data ]= E[ E[ Value | Data ] ]= 130• Terminate project if value<0• NB! The trial is useful only if it separates positive and negative
values.
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0%
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After n=60 patients
x=0 x=1 x=2 etc
0%
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40%
60%
80%
100%
After n=20 patients
x=0 etc
-600
-400
-200
0
200
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-600
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-200
0
200
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ValueValue After n=20 patientsAfter n=60 patients
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How to choose n and m?• Add cost of safety trial• Maximizing E[Value] over all possible n’s, m’s• Do we need a placebo group?
• Adaptive design of safety trial• allocation fraction to placebo group may
depend on data• Adaptive design of next phase
• checking for AE X during study
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Dose-responseexample
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A new drug
• has pros and cons• … and some uncertainty in the assessment
thereof• It is important to study each dimension
(efficacy, different types of safety issues) separately
• But a combined analysis may also be useful• May this help sponsor-regulator
communication?
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0
0,05
0,1
0,15
0,2
0 1 2 3 4 5
Exposure
Rate / Loss fcn
Lack of effect
AENet loss
Weighted net loss
Inspired by Marie Cullberg’s PhD thesis
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Don’t trust your DA blindly!• Check robustness• Question the assumptions• Let the decision-makers, not the DA model,
determine the final decision
• DA helps decision-makers• by structuring the problem• exploring logical consequences of
assumptions• facilitate communication