THE USE OF PRECISION TREE TO AID COMPLEX DECISION-MAKING IN DRUG DEVELOPMENT
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1 PROJECT & PORTFOLIO VALUE CREATION THE USE OF PRECISION TREE TO AID COMPLEX DECISION-MAKING IN DRUG DEVELOPMENT 1 What are the objectives of this presentation? 1) How to decompose a complex investment decision in a logically consistent manner 2) How to challenge SME judgment in data elicitation 3) How to conduct a defensible program evaluation and evaluate the sensitivity of the dominance of a policy suggestion to risks and uncertainties 4) How to facilitate good decision-making by striving for approximate data correctness as opposed to absolute precision 5) How to apply the Decision Quality cycle to good decision-making 2
2 Case study Drugs R Us Drugs R Us, a Biopharmaceutical company, faces a decision to invest in either (a) $325M in a lead molecule ABC for the treatment of Alzheimer s Disease (AD) or (b) $550M in a lead molecule XYZ for the treatment of Mild Cognitive Impairment (MCI) and General Anxiety Disorder (GAD) Both compounds have clinical and regulatory risks associated with their development and regulatory approval and, because of the rapidly changing competitive environment, there are huge commercial uncertainties associated with their market uptake With the use of Palisade s Precision Tree, a multi-phase Decision Tree was constructed for each decision alternative. For the purposes of this presentation, this complex Decision Tree was subsequently collapsed and displayed by a single R&D node (to represent clinical and regulatory risks) along with a single Commercial node (to represent commercial uncertainties) Drugs R Us intends to make its decision on the basis of risk-adjusted NPV (enpv) but, because the company recognizes that the data are imperfect, it wishes to test the sensitivity of the policy suggestion with respect to risk and uncertainty estimates before making a final decision 3 Precision Tree representation of decomposed investment decision problem 4
3 Background data and information 5 Biases impact the goodness of data and information which software systems can use to create an illusion of credibility MOTIVATIONAL BIAS COGNITIVE BIAS Personal interest & commitment Availability of information Perceived reward structures Representativeness of information Expert bias Anchoring and adjustment Inexpert bias 6
4 Precision Tree representation of strategic alternatives and accompanying data 7 Policy suggestion Invest in MCI & GAD program 8
5 Definition of program success is Success in MCI & GAD How is the policy suggestion affected? 9 Definition of program success is Success in at least MCI How is the policy suggestion affected? 10
6 Definition of program success is Success in at least GAD How is the policy suggestion affected? 11 Strategy A a closer look at the risk associated with AD clinical & regulatory success 12
7 Sensitivity of policy suggestion to probability of AD clinical & regulatory success (I) breakpoint at around 65% 13 Sensitivity of policy suggestion to probability of AD clinical & regulatory success (II) breakpoint at 64.7% 14
8 Strategy A a closer look at the probability of AD clinical & regulatory success and forecasted high AD commercial success 15 Sensitivity of policy suggestion to probability of AD clinical & regulatory success and forecasted high AD commercial success 16
9 Sensitivity of policy suggestion to probability of AD clinical & regulatory success and forecasted high AD commercial success 17 A closer look at the probabilities of high AD commercial success and high MCI & GAD commercial success 18
10 Sensitivity of policy suggestion to probabilities of high AD commercial success and high MCI & GAD commercial success 19 A closer look at forecasted high AD commercial success and high MCI commercial success 20
11 Sensitivity of policy suggestion to forecasted high AD commercial success and high MCI commercial success (I) 21 Sensitivity of policy suggestion to forecasted high AD commercial success and high MCI commercial success (II) 22
12 Summary table of impact of sensitivity analysis to policy suggestion Pursue MCI & GAD program RISK / UNCERTAINTY PARAMETER ELICITED / FORECASTED VALUE BREAKPOINT VALUE PROBABILITY OF CLINICAL & REGULATORY SUCCESS p(ad Success) 60% 64.7% PROBABILITY OF HIGH COMMERCIAL FORECAST p(high AD Forecast) HIGH COMMERCIAL FORECAST High AD Forecast PROBABILITY OF HIGH COMMERCIAL FORECAST High MCI & GAD Forecast HIGH COMMERCIAL FORECAST High MCI Forecast 75% $1,000 M 50% $1,350 M If p(ad Success) > 75% and High AD Forecast > $1,400 M, change Policy Suggestion. Otherwise, Policy Suggestion may change If p(high AD Forecast) < 80% and p(high MCI & GAD Forecast) > 30%, do not change Policy Suggestion. Otherwise, Policy Suggestion may change Irrespective of value of High MCI forecast, if High AD Forecast > $1,200 M, change Policy Suggestion 23 Decision Quality a recommended chain of events utilized to facilitate good decision-making SET DECISION FRAME EXECUTE & TRACK DECISION OUTCOME CREATE STRATEGIC ALTERNATIVES DECISION QUALITY CYCLE APPLY LOGICALLY CORRECT REASONING VALIDATE DATA & INFORMATION ESTABLISH DESIRED VALUES & TRADE-OFFS 24 Attributed to SDG
13 Type I and II errors describe the balance between stopping good investments too soon and continuing poor ones indefinitely 25 Were the objectives of this presentation achieved? 1) How to decompose a complex investment decision in a logically consistent manner 2) How to challenge SME judgment in data elicitation 3) How to conduct a defensible program evaluation and evaluate the sensitivity of the dominance of a policy suggestion to risks and uncertainties 4) How to facilitate good decision-making by striving for approximate data correctness as opposed to absolute precision 5) How to apply the Decision Quality cycle to good decision-making 26
14 Thank you for your time and attention, and I wish you success with using Precision Tree as an aid to complex decision-making BUILDING CAPABILITIES AND VALUE, ONE BLOCK AT A TIME Address: 37 Terrell Drive, Washington Crossing, PA 18977, USA Landline: (215) ; Mobile: (267) Web: LinkedIn: rbayney@ppvc.net; bayney@sas.upenn.edu 27
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