New Pr Ofesy Conference Presentation

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Background to Wavedata\'s approaches to forecasting generic pharma prices after loss of exclusivity / patent expiry and generic launch

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  • Research project Trying to find the hidden patterns behind Long term Points – interesting / surprising / unexpected
  • How do companies set or predict prices. What would you do?
  • A guess could be based on a particular competitor, or a key supplier, or an average, but it is still at heart a guess
  • Trend lines can be added to what data you have, but often more than one line can be fitted to the same trend, and if you only have a few months of post patent generic prices, many trends could be fitted to the same data – each one a guess.
  • The price went up, as it was following a hidden pattern, not immediately obvious to the uninformed
  • Trend lines can be added to what data you have, but often more than one line can be fitted to the same trend, and if you only have a few months of post patent generic prices, many trends could be fitted to the same data – each one a guess.
  • Trend lines can be added to what data you have, but often more than one line can be fitted to the same trend, and if you only have a few months of post patent generic prices, many trends could be fitted to the same data – each one a guess.
  • But sometimes they don't
  • The range of decline curves possible is very wide
  • Information revolution Bill gates Email Design around the planet Ebay Lack of science in one area of pharma – pricing Aunt story
  • The range of decline curves possible is very wide
  • Each of the 100+ decline curves we looked at was based on a pattern
  • Each could be modelled
  • Each could be connected with the underlying factors
  • Even the rises in price due to availability and price could be predicted
  • Even the rises in price due to availability and price could be predicted
  • The range of decline curves possible is very wide
  • The range of decline curves possible is very wide
  • Patterns are complex but predictable Not as complicated as shares But pharmaceuticals is an island – doesn't pick up techniques from other industries easily Why has no one done it before – not enough pricing history Norton healthcare SPSS experience
  • So the method works Will get better as more examples are analysed Sugar traders / stocks and shares But bounces still an issue ….. But what about other markets
  • Nothing is new But pharma is still left with vulnerability in those companies that don’t take this on board No reason why techniques won’t work in other states
  • Pebble in a pond
  • New Pr Ofesy Conference Presentation

    1. 1. Generic Prices Forecasting and Mega Trends
    2. 3. How do companies forecast generic prices? Time Price Generic Launch
    3. 4. Some just react to market changes
    4. 5. Fitting trends to prices
    5. 6. But reality was different
    6. 7. Same Therapeutic Cat Same Therapeutic Category, But Different Decay Rates Selective Serotonin Re-Uptake Inhibitors
    7. 8. Same Market Value Same Value, But Different Decay Rates £450,000 - £550,000
    8. 9. Same molecule Same Molecule But No Uniformity Fluconazole Caps
    9. 10. But actual patterns are very complex
    10. 11. SWAGs <ul><li>Sophisticated Wild Ass Guesses </li></ul>
    11. 13. Data Collection <ul><li>Wavedata founded in 2000 </li></ul><ul><li>60,000 hours of data entry </li></ul><ul><li>160 wholesalers and suppliers </li></ul><ul><li>Thousands of generic products </li></ul><ul><li>2 years of analysis </li></ul>
    12. 14. Trend in average generic price
    13. 15. Each product follows a pattern
    14. 16. Patterns & Relationships £ ?  Generic Price Volume Market Share Value Brand Generic Spilt No of Manufacturers Reimbursement 
    15. 17. Statisticians found each product can be modelled
    16. 18. Multiple models were produced, one for each product
    17. 19. Each with its own formula
    18. 20. First Model (2005) <ul><li>80 products analysed </li></ul><ul><li>Linear dynamic model </li></ul><ul><li>3 forecast models </li></ul><ul><li>A, B and C </li></ul><ul><li>Based on statistical coefficients </li></ul>
    19. 21. Further development (2006) <ul><li>Another year of modelling </li></ul><ul><li>120 products analysed </li></ul><ul><li>N on-linear polynomial model </li></ul><ul><li>Adding Reimbursement arguments </li></ul><ul><li>Including Tariff M </li></ul>
    20. 22. Current model completed (2007) <ul><li>Works for 99% of products </li></ul><ul><li>No therapeutic adjustment needed </li></ul><ul><li>No strength adjustment needed </li></ul><ul><li>Integrated into a web site </li></ul>
    21. 24. Does it work? <ul><li>Can generic prices really be forecast? </li></ul><ul><ul><li>Before </li></ul></ul><ul><ul><li>During </li></ul></ul><ul><ul><li>Actuality </li></ul></ul>
    22. 32. Other Markets <ul><li>Model can be adapted for new markets </li></ul><ul><li>Different coefficients for each market </li></ul><ul><li>USA </li></ul><ul><li>EU States </li></ul>
    23. 33. USA
    24. 34. USA vs UK
    25. 35. Other Products
    26. 37. The ‘Dead Cat Bounce’
    27. 38. Key Bounce factors <ul><li>Cost of goods </li></ul><ul><li>Manufacturer withdrawal </li></ul><ul><li>Short or long residual life </li></ul><ul><li>Holiday link? </li></ul><ul><li>Bounces are visible side of seasonality? </li></ul><ul><li>Disease timings – ie hay fever </li></ul>
    28. 39. How often bounces happen <ul><li>282 products analysed </li></ul><ul><li>42 products bounced once </li></ul><ul><li>6 products bounced twice </li></ul><ul><li>4 products bounced three times </li></ul><ul><li> 18% of products bounced </li></ul>
    29. 40. Bounces after generic launch
    30. 41. Bounces – the real picture
    31. 42. Bounce frequency
    32. 43. Seasonality – Omeprazole Apr 02- Mar 06
    33. 44. Seasonality - Omeprazole Oct 02- Sep 06
    34. 45. Seasonality - Ciprofloxacin Jan 03 – Dec 05
    35. 46. Seasonality - Levothyroxine Oct 00-Sep 06
    36. 47. Seasonality - Atenolol Oct 00-Sep 06 Jan 01- Dec 05
    37. 48. Seasonality - Simvastatin Oct 03-Sep 06
    38. 49. Seasonality - Lisinopril Jan 01 – Dec 05
    39. 50. Clusters <ul><li>8 Clusters seen so far </li></ul><ul><li>Some product specific </li></ul><ul><li>Some not </li></ul>
    40. 51. Clusters 1 - 4
    41. 52. Clusters 5 - 8
    42. 53. Highs and Lows <ul><li>Low price point </li></ul><ul><ul><ul><li>February </li></ul></ul></ul><ul><ul><ul><li>June </li></ul></ul></ul><ul><ul><ul><li>November </li></ul></ul></ul><ul><li>High price points </li></ul><ul><ul><ul><li>April </li></ul></ul></ul><ul><ul><ul><li>August </li></ul></ul></ul><ul><ul><ul><li>December </li></ul></ul></ul>
    43. 54. Possible reasons <ul><li>Highs </li></ul><ul><ul><li>When commercial people are </li></ul></ul><ul><ul><li>on holiday </li></ul></ul><ul><li>Lows </li></ul><ul><ul><li>When commercial people are </li></ul></ul><ul><ul><li>all working </li></ul></ul><ul><li>But what about the others? </li></ul>
    44. 55. Summary <ul><li>Natural decline √ </li></ul><ul><li>Reimbursement √ </li></ul><ul><li>Seasonality √ </li></ul><ul><li>UK √ </li></ul><ul><li>Other Markets ? </li></ul>
    45. 56. Many Thanks www.wavedata.biz

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