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Managing Tough Decisions With Decision Analysis INFORMS Southern California Chapter Meeting

Managing Tough Decisions With Decision Analysis INFORMS Southern California Chapter Meeting California State University, Northridge Phil Beccue. Competing Objectives. Patient Benefit. Long-term Financial value. Wall Street. Many needs, limited resources.

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Managing Tough Decisions With Decision Analysis INFORMS Southern California Chapter Meeting

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  1. Managing Tough Decisions With Decision Analysis INFORMS Southern California Chapter Meeting California State University, Northridge Phil Beccue

  2. Competing Objectives Patient Benefit Long-term Financialvalue Wall Street Many needs,limited resources Organizationimage/publicperception Novel products Dispersed Information/Multiple Stakeholders/ Differing Opinions Projects that won’t die Risk/Uncertainty Timing/Staging complexities ? A B C Yr What makes resource allocation decisions difficult?

  3. Ostrich Approach Locked Door Approach Fair Share Approach FOR SALE DO NOTDISTURB Washington Monument Approach Dominant Personality Approach Squeaky Wheel Approach Trust Us Approach R I S K Squeeeak Many companies use informal approaches when allocating resources which may not be effective

  4. Decision Analysis (DA) addresses the challenges of real-world decisions • Provides a method to decompose complex problems • Broad set of alternatives • Specific agreement (up front) on criteria • Explicit accounting of uncertainty • Offers a set of tools and processes to bring clarity to the best course of action • Based on foundational axioms of utility theory • Is a prescriptive approach to decision-making applied to important, real-world problems

  5. The Decision Analysis process provides a guide to think systematically about R&D decisions • Decision analysis is a rigorous, transparent, quantitative approach for balancing the difficult tradeoffs inherent in R&D decisions. • A formal process provides a common language for thinking and communicating about decisions within a multidisciplinary team. Modeling and Data Collection Communication and Integration Decision Problem Action Structuring Evaluation

  6. How should we develop and commercialize Leapogen for the athletic jumping indication? A case example illustrates the application of DA to a tough decision for a drug company • Phase 2 trials for Leapogen to address an unmet medical need (jumping ability) are nearly complete • High efficacy shown in a few of the 17 major potential clinical settings • Management had differing views on the best way to proceed to develop and commercialize Leapogen • Senior management review meeting in 2 months • We started by carefully crafting a decision statement to keep the team focused:

  7. We took a comprehensive look at 8 strategies and included all significant issues • 8 well-defined Strategies • Approved Label • Narrow • Medium • Broad • Probability of Tech Success • Probability of Reg Approval • Launch Timing • Price • R&D Costs • S&M Costs • COGS • Patient Population by Indication • Treated patients • Patient growth rate • Disease Severity • Therapeutic Penetration • Competition • Market Share • Marketing Focus

  8. Clinical Clinical Clinical Clinical Clinical Clinical Clinical Setting A (Basketball) Setting B (Soccer) Setting C (Volleyball) Setting D (Long Jump) Setting E (High Jump) Setting F (Hurdles) Setting G (Rugby) Ph 2 Ph 2 Ph 2 Ph 2 Ph 2 Ph 2 Ph 2 Ph 3 Ph 3 Ph 3 Ph 2/3 Ph 3 Ph 3 Ph 3 INV IND INV IND Ph 3 INV IND INV IND INV IND None None None INV IND None None None None A strategy table narrowed the feasible alternatives to 8 clearly defined development strategies Strategy Name Dev Cost Strategy 1 $128M Strategy 2 $140M Strategy 3 $ 82M Strategy 4 $ 40M Strategy 5 $120M Strategy 6 $ 103M Strategy 7 $ 25M Strategy 8 $180M

  9. We carefully defined 3 label outcomes for each strategy by specifying the clinical setting included KEY a – basketball b – soccer c – volleyball d – long jump e – high jump f – hurdles g – rugby h – ballet i – gymnastics j – figure skating k – …

  10. The decision tree specified the scenarios to be analyzed for each strategy The model calculated NPV for over 4 million scenarios!

  11. An influence diagram defined the key input requirements to compute NPV Decision Uncertainty Value

  12. Probability 10% of area 10% of area 500 1000 2000 We specified uncertain inputs using probability distributions

  13. Technical success probabilities were assessed through structured conversations and compared to industry benchmark data Launch .91 .73 .46 .60 New, Active Substances (NAS) CMR Int’l Data, 1997

  14. The probabilities of technical and regulatory success varied by strategy Probability Technical Success Probability of Regulatory Success Given Label Strategy

  15. The top strategy (#4) provides over $150M additional value than the status quo (#8) strategy Size of bubble is proportional to expected peak sales. 7 5 4 2 1 3 6 8

  16. Probability-weighted average = $10 million There is only a 10% chance of getting an NPV greater than $100M There is a 30% chance of launch The expected NPV for Strategy 4 is $10M, including all technical and commercial risks

  17. Assuming technical success, peak year revenue could vary from $100 to $250 M, with expected sales of $150M Expected Value Probability Density 75 100 125 150 175 200 225 250 275 300 325 Peak Sales ($M)

  18. Each bar in the tornado diagram shows the impact on commercial value of moving one uncertain input across its range of uncertainty while holding all other inputs to the base case. The base value ($270 MM) is calculated by setting all uncertain inputs to their base case. The key drivers of risk for Strategy 4 are Competition, Peak Share for Basketball, and Label Competition Peak Share Basketball Label Severity of Disease Long Jump Peak Share High Jump Peak Share Long Jump Severity of Disease Long Jump Peak Share Soccer Severity of Disease High Jump Peak Share Volleyball Peak Share Hurdles Severity of Disease Volleyball Assumes technical and regulatory success Peak Share Gymnastics Peak Share Ballet 0 50 100 150 200 250 300 350 400 Peak Sales ($M)

  19. Revenues for Strategy 4 are uncertain, and key contributors of value are clinical settings A, D, and E

  20. Probability Peak Sales ($M) Risk/return tradeoffs for each strategy were made explicit Strategy 8 Strategy 4 Strategy 7 0 50 100 150 200 250 300 350

  21. Strategy 3 will provide positive value if the chance of success exceeds 50% Nominal Probability = 25%

  22. The optimal marketing focus depends on the outcome of future uncertainties Marketing Region 1 Competition Focus Timing Region 2 Early Region 3 Bold line indicates highest NPV path Label Region 4 Narrow Region 1 Region 2 Late Region 3 Region 4 Competition Timing Region 1 Region 2 S1 Medium Early Region 3 S2 Region 4 S3 Leapogen Region 1 Strategy S4 Region 2 S5 Late Region 3 S6 Broad Region 4 S8

  23. Key insights from the strategic analysis • Strategy 4 (a focused strategy) has best overall value • Peak sales is $150 million • Optimal marketing focus should be determined closer to launch • High value indications (% of total value): • Clinical setting A (62%) • Clinical setting E (26%) • Clinical setting D (12%) • Chance of success = 30% • Clinical setting G is not as critical as once thought

  24. The initial DA had a significant impact at Amgen • Jumping ability is a viable indication for Leapogen • Structuring the complex set of development options provided direction and a clear development plan • Senior management agreed to follow the recommended strategy of a focused program • The recommended strategy gave $150M additional value over the status quo strategy • Key drivers were identified as needing further investigation; senior management asked to update the decision analysis in the future • A few months later, the team asked our group to translate the decision model into a forecasting tool for ongoing use • We have performed additional strategic projects for Leapogen based on the initial work

  25. Decision Board Problem Structuring Modeling/Data Collection Evaluation Communicationand Integration Decision Analysis Team Information Experts Time It is critical to keep management informed at each step of the process Action !

  26. Documents the decision process Information collection is focused and efficient Helps resolve conflicts and debates Fewer “surprises” because uncertainty is explicitly considered Avoids common pitfalls in analyzing complex situations solving the wrong problem analyzing what is known rather than what is important getting lost in the process More time required of decision-makers More time for analysis team Possible discomfort with new process Reveals logic of decision confidential information lack of knowledge embarrassing motivations The benefits of using decision analysis should be weighed against the costs Benefits Costs

  27. The significant problems we face cannot be solved at the same level of thinking we were at when we created them. - Albert Einstein

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