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SKIM presentation EphMRA 2012: Managing uncertainty in forecast
 

SKIM presentation EphMRA 2012: Managing uncertainty in forecast

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SKIM has been using Monte Carlo simulation to help clients with forecasting the uptake of branded generics, biosimilars and targeted medicine – areas of increasing complexity and uncertainty. On ...

SKIM has been using Monte Carlo simulation to help clients with forecasting the uptake of branded generics, biosimilars and targeted medicine – areas of increasing complexity and uncertainty. On behalf of SKIM, our experts Dirk Huisman and John Ashraf presented this approach at EphMRA's 2012 Conference in Paris. They talked about the principles of Monte Carlo Simulation and how it can be applied to forecast the uptake of your product.

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    SKIM presentation EphMRA 2012: Managing uncertainty in forecast SKIM presentation EphMRA 2012: Managing uncertainty in forecast Presentation Transcript

    • Managing uncertainty in the forecastof the uptake of branded generics,biosimilars and targeted medicine Dirk Huisman & John Ashraf on behalf of SKIM | EphMRA 2012
    • The changing REALITY of “Pharma”Blockbuster
    • The changing REALITY of “Pharma”Post - Blockbuster
    • The changing REALITY of “Pharma”Divergent views
    • The changing context of MR • big 55%  35% • margin 29%  8% • process redesign • less comprehensive projects • more uncertainties • deterministic  stochastic
    • Deterministic ↔ Stochastic process SuccessDeterministic: Time  Success Of a certainStochastic: probability Time 
    • Types of uncertaintyStatistical uncertainty: sample-related uncertainty based onhow the sample represents the population.• Classical statistical methods apply to determine confidence intervalData uncertainty:• Conflicting data sources (e.g. population size, testing rates, etc…)• Uncertain product profile (e.g. outcome of clinical trials)• EMA ruling re. biosimilars (e.g. trials needed, extrapolation, etc..)
    • Example business case*
    • Example business case*• Product Eiffel is being launched for metastatic lung cancer; it is primarily targeted for a certain mutation, which depends on testing – There are conflicting data sources for mutation testing rates• Phase III clinical trials are close to completion – Final safety figures are yet to be known• Furthermore, primary competition is expected to become generic, making market access conditions less certain• Conflicting market access and price sensitivity data *Adapted from a real project case
    • Forecasting parameters(simplified) Population Market Volume % diagnosed * Impact of competition Eiffel patient share* being generic % tested for mutation Compliance* and eligible for Eiffel access/pricing Duration of treatment* product Eiffel* Eiffel awareness* Primary research based, backed up / contradicted by secondary data
    • Certainty in the parameter values Population Market Volume% diagnosed Impact of competition Eiffel patient share being generic% tested for mutation Complianceand eligible for Eiffel access/pricing Duration of treatmentproduct Eiffel Eiffel awareness low medium high
    • Dealing with uncertainty: Monte Carlo• First used by Ulam and Von Neumann for simulations during development of hydrogen bomb (Manhattan project)• Widely used in different fields For our case, we will use Monte Carlo to deal with data uncertainty: • Uncertain mutation testing rates • Product safety is still unknown, different possible share outcomes • Competition may become generic, might have a negative effect on Eiffel share • We will use Monte Carlo technique to deal with all forecasting parameters taking into account assumed parameter certainty levels
    • Using Monte Carlo in forecastingIn a nutshell:1. For every forecast parameter, pick a random value in the range considered2. Calculate a full forecast based on the picked values3. Repeat… for 1,000 (or more) timesHow to pick a value from a range; uncertainty distributions1. Define a range for input (instead of one value) – For example: mutation testing rate between 50-70%2. Decide on how certain you are that the parameter will be one value (usually the mean) as compared to the rest of the range3. Build distribution using certainty assumption, use it for random draws
    • Distribution types Distribution of valuesUncertainty Formula for random draws around the mean Norminv (rand(),(max-min)/2 + minLowMedium (rand()+rand())/2*(max-min) + minHigh min + rand()*(max-min)
    • Distribution types Distribution of valuesUncertainty Formula for random draws around the mean Norminv (rand(),(max-min)/2 + minLow • Used when high confidence in one value in the rangeMedium (rand()+rand())/2*(max-min) + min • Typically used with survey- acquired information • Example:High min + rand()*(max-min) patients Proportion of compliant
    • Distribution types Distribution of valuesUncertainty Formula for random draws around the mean Norminv (rand(),(max-min)/2 + minLow • Used when different likelihood for certain discreet scenarios • Typically used with manufacturer-Medium influenced information (rand()+rand())/2*(max-min) + min • Example: Likelihood of two profile outcomes of clinical trials or expectedHigh min + rand()*(max-min) budget for awareness campaign
    • Distribution types Distribution of valuesUncertainty Formula for random draws around the mean • Used when multiple data sources with no way to verify which to use Norminv (rand(),(max-min)/2 + minLow • Typically used with emerging markets or recent market changes with no reliable dataMedium • (rand()+rand())/2*(max-min) + min Example: Proportion of patients being tested with a newly available diagnosticHigh min + rand()*(max-min)
    • Applying Monte Carlo to our example
    • Applying Monte Carlo to our example Population Market Volume% diagnosed Impact of competition Eiffel patient share being generic% tested for mutation Complianceand eligible for Eiffel access/pricing Duration of treatmentproduct Eiffel Eiffel awareness Revenue distribution
    • Action standardSetting an action standard is key in Monte Carlo simulation• Define the target revenue (or other KPI)• Agree on the probability or risk (% likelihood of achieving the KPI) – For example: 70% chance of reaching $ 520M and 90% chance of reaching $ 390MThe output distribution from Monte Carlo can be used tosee if the go /no go action standard is met• From there a go or no go decision can be made, particularly helpful in in-licensing deals
    • Revenue forecast 1000 • Provides a probability 900 likelihood of hitting a certainCumulative revenue forecast (million) 800 90% probability range forecast 700 600 • Helps narrow down the best- worst case range 500 – Looking at 90% probability, 400 the revenue will fall between 390 and 520 M 300 200 • Guidance in uncertain times 100 and when risk assessments 0 need to be made 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
    • Practical learnings• Scenario analyses and discussing different conditions & outcomes enables substantiated mapping of risks and opportunities, and of the feasibility of the initiative. Collectively you identify what is most critical.• Decision makers are familiar with concept of probabilities, but ultimately they want one answer. Often they are not inclined to commit to a range nor to the probability. It is critical to collect the probabilities form the ones you might know, independent of the one final answer.• Key to agree on action standards before initiating project.
    • Practical learningsDespite the fact that reality in pharma is stochastic, thedominant attitude is deterministic.Monte Carlo helps to understand the risks and the relatedoutcomes, and it helps fueling the interaction and thedebate.You help reducing the uncertainties and cope with it
    • Any uncertainties?Dirk Huisman, Chairman SKIM | d.huisman@skimgroup.com or +31 10 282 3535John Ashraf, Senior Methodologist SKIM | j.ashraf@skimgroup.com or +31 10 282 3516