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Forecasting branded and generic pharmaceuticals

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  • Nikolopoulos, Konstantinos
  • Buxton, Samantha
  • Khammash, Marwan
  • Stern, Philip

Abstract

We forecast UK pharmaceutical time series before and after the time of patent expiry. This is a critical point in the lifecycle, as a generic form of the product is then introduced into the market, while the branded form is still available for prescription. Forecasting the numbers of units of branded and generic forms of pharmaceuticals dispensed is becoming increasingly important, due to their huge market value and the limited number of new ‘blockbuster’ branded drugs, as well as the imposed cost for national healthcare systems like the NHS. In this paper, eleven methods are used to forecast drug time series, including diffusion models (Bass model & RPDM), ARIMA, exponential smoothing (Simple and Holt), naïve and regression methods. ARIMA and Holt produce accurate short term (annual) forecasts for branded and generic drugs respectively, while for the more strategic horizons of 2–5 years ahead, naïve with drift provides the most accurate forecasts.

Suggested Citation

  • Nikolopoulos, Konstantinos & Buxton, Samantha & Khammash, Marwan & Stern, Philip, 2016. "Forecasting branded and generic pharmaceuticals," International Journal of Forecasting, Elsevier, vol. 32(2), pages 344-357.
    • Handle: RePEc:eee:intfor:v:32:y:2016:i:2:p:344-357
    DOI: 10.1016/j.ijforecast.2015.08.001
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    as
    1. Polli, Rolando & Cook, Victor, 1969. "Validity of the Product Life Cycle," The Journal of Business, University of Chicago Press, vol. 42(4), pages 385-400, October.
    2. Marc Fischer & Peter Leeflang & Peter Verhoef, 2010. "Drivers of peak sales for pharmaceutical brands," Quantitative Marketing and Economics (QME), Springer, vol. 8(4), pages 429-460, December.
    3. Richard G. Frank & David S. Salkever, 1997. "Generic Entry and the Pricing of Pharmaceuticals," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 6(1), pages 75-90, March.
    4. William E. Cox & Jr., 1967. "Product Life Cycles as Marketing Models," The Journal of Business, University of Chicago Press, vol. 40, pages 375-375.
    5. Bauer, Hans H. & Fischer, Marc, 2000. "Product life cycle patterns for pharmaceuticals and their impact on R&D profitability of late mover products," International Business Review, Elsevier, vol. 9(6), pages 703-725, December.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
    8. Verniers, I.W.J. & Stremersch, S. & Croux, C., 2011. "The Global Entry of New Pharmaceuticals: A Joint Investigation of Launch Window and Price," ERIM Report Series Research in Management ERS-2011-010-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. Ricardo Montoya & Oded Netzer & Kamel Jedidi, 2010. "Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability," Marketing Science, INFORMS, vol. 29(5), pages 909-924, 09-10.
    10. Gary L. Lilien & Ambar G. Rao & Shlomo Kalish, 1981. "Bayesian Estimation and Control of Detailing Effort in a Repeat Purchase Diffusion Environment," Management Science, INFORMS, vol. 27(5), pages 493-506, May.
    11. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    12. Andrew T. Ching & Masakazu Ishihara, 2012. "Measuring the Informative and Persuasive Roles of Detailing on Prescribing Decisions," Management Science, INFORMS, vol. 58(7), pages 1374-1387, July.
    13. Ambar G. Rao & Masataka Yamada, 1988. "Forecasting with a Repeat Purchase Diffusion Model," Management Science, INFORMS, vol. 34(6), pages 734-752, June.
    14. Scott Armstrong, J. & Brodie, Roderick J. & McIntyre, Shelby H., 1987. "Forecasting methods for marketing: Review of empirical research," International Journal of Forecasting, Elsevier, vol. 3(3-4), pages 355-376.
    15. Rajshree Agarwal & Michael Gort, 2002. "Firm and Product Life Cycles and Firm Survival," American Economic Review, American Economic Association, vol. 92(2), pages 184-190, May.
    16. Peter N. Golder & Gerard J. Tellis, 2004. "Growing, Growing, Gone: Cascades, Diffusion, and Turning Points in the Product Life Cycle," Marketing Science, INFORMS, vol. 23(2), pages 207-218, December.
    17. Darroch, Jenny & Miles, Morgan P., 2011. "A research note on market creation in the pharmaceutical industry," Journal of Business Research, Elsevier, vol. 64(7), pages 723-727, July.
    18. Liu, Qiang & Gupta, Sachin, 2011. "The impact of direct-to-consumer advertising of prescription drugs on physician visits and drug requests: Empirical findings and public policy implications," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 205-217.
    19. Petropoulos, Fotios & Makridakis, Spyros & Assimakopoulos, Vassilios & Nikolopoulos, Konstantinos, 2014. "‘Horses for Courses’ in demand forecasting," European Journal of Operational Research, Elsevier, vol. 237(1), pages 152-163.
    20. Verniers, Isabel & Stremersch, Stefan & Croux, Christophe, 2011. "The global entry of new pharmaceuticals: A joint investigation of launch window and price," International Journal of Research in Marketing, Elsevier, vol. 28(4), pages 295-308.
    21. Thomas Aronsson & Mats Bergman & Niklas Rudholm, 2001. "The Impact of Generic Drug Competition on Brand Name Market Shares – Evidence from Micro Data," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 19(4), pages 423-433, December.
    22. Fruchter, Gila E. & Van den Bulte, Christophe, 2011. "Why the Generalized Bass Model leads to odd optimal advertising policies," International Journal of Research in Marketing, Elsevier, vol. 28(3), pages 218-230.
    23. Gonzalez, Jorge & Sismeiro, Catarina & Dutta, Shantanu & Stern, Philip, 2008. "Can branded drugs benefit from generic entry? The role of detailing and price in switching to non-bioequivalent molecules," International Journal of Research in Marketing, Elsevier, vol. 25(4), pages 247-260.
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