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

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Editor's Notes

  • #3: Research project Trying to find the hidden patterns behind Long term Points interesting / surprising / unexpected
  • #4: How do companies set or predict prices. What would you do?
  • #5: A guess could be based on a particular competitor, or a key supplier, or an average, but it is still at heart a guess
  • #6: 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.
  • #7: The price went up, as it was following a hidden pattern, not immediately obvious to the uninformed
  • #8: 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.
  • #9: 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.
  • #10: But sometimes they don't
  • #11: The range of decline curves possible is very wide
  • #13: Information revolution Bill gates Email Design around the planet Ebay Lack of science in one area of pharma pricing Aunt story
  • #14: The range of decline curves possible is very wide
  • #15: Each of the 100+ decline curves we looked at was based on a pattern
  • #16: Each could be modelled
  • #18: Each could be connected with the underlying factors
  • #19: Even the rises in price due to availability and price could be predicted
  • #20: Even the rises in price due to availability and price could be predicted
  • #21: The range of decline curves possible is very wide
  • #22: The range of decline curves possible is very wide
  • #24: 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
  • #32: 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
  • #37: Nothing is new But pharma is still left with vulnerability in those companies that dont take this on board No reason why techniques wont work in other states
  • #41: Pebble in a pond