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A Quantitative Approach to Clinical Development

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?.

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A Quantitative Approach to Clinical Development

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  1. A Quantitative Approach to Clinical Development Carl-Fredrik Burman, PhD Statistical Science Director AstraZeneca R&D, Sweden

  2. A new paradigm (?)

  3. How should we get there? Alternative designs (adaptive, cross-over, “traditional”) Where are we? To where do we want to go? Decision Analysis (DA) to optimize design, based on model & preferences Modeling Preferences Simulations

  4. Study designdecisions

  5. 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, D, 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 D=30 instead. S: “Then the required sample size is 469. M: Excellent The medics have taken care of population, duration, variable, etc.

  6. How should we get there? Alternative designs (adaptive, cross-over, “traditional”) Where are we? To where do we want to go? Decision Analysis (DA) to optimize design, based on model & preferences Modeling Preferences Simulations

  7. Example of astudy designdecision Thanks to Claes Ekman & Björn Bältsjö

  8. 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?

  9. 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

  10. 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

  11. Single-arm trial • Historical data exist for placebo group • Alternative trial with n=60, m=0

  12. 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

  13. Single-arm safety trial n=60 pat’s; x=3 AEs Prior distribution 100% p=0.10 p=0.03 80% 60% 40% p=0.00 20% 0% Posterior = Prior + Data

  14. Prior distribution 100% p=0.10 p=0.03 80% 60% 40% p=0.00 20% 0% Posterior if x=3, n=60 100% p=0.10 80% p=0.03 60% 40% 20% p=0.00 0% 1

  15. Before trial / Prior p=0.10 p=0.03 p=0.00 After n=60 patients p=0.10 p=0.03 p=0.00 x=0 x=2 etc x=1

  16. Before trial / Prior After n=20 patients x=0 etc After n=60 patients Ideal (infinite info) x=0 x=2 etc x=1

  17. 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

  18. Total value ofsuggested safety trial (n=60) E[Value] = … x Probability Project value 0 32.2% 433 1 24.9% 280 2 14.4% 16 3 9.2% -169 4 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.

  19. After n=60 patients x=0 x=2 etc x=1 After n=20 patients x=0 etc Value After n=60 patients Value After n=20 patients

  20. 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

  21. Dose-responseexample

  22. 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?

  23. Rate / Loss fcn Weighted net loss Net loss AE Lack of effect Exposure Inspired by Marie Cullberg’s PhD thesis

  24. 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

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