A Quantitative Approach to Clinical Development

Carl-Fredrik Burman, PhDStatistical Science DirectorAstraZeneca R&D, Sweden

A new paradigm (?)

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

Study designdecisions

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.

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

Example of astudy design

decision

Thanks to Claes Ekman & Björn Bältsjö

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?

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

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

Single-arm trial

• Historical data exist for placebo group• Alternative trial with n=60, m=0

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

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

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

0%

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60%

80%

100%

0%

20%

40%

60%

80%

100%

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

0%

20%

40%

60%

80%

100%Before trial / Prior

0%

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100%

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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100%After n=20 patients

x=0 etc

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

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.

0%

20%

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80%

100%

After n=60 patients

x=0 x=1 x=2 etc

0%

20%

40%

60%

80%

100%

After n=20 patients

x=0 etc

-600

-400

-200

0

200

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600

-600

-400

-200

0

200

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600

ValueValue After n=20 patientsAfter n=60 patients

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

Dose-responseexample

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?

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

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