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Forecasting Branded and Generic Pharmaceuticals

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Forecasting Branded and Generic Pharmaceuticals. / Nikolopoulos, Kostas; Buxton, Samantha ; Khammash, Marwan et al.
In: International Journal of Forecasting, Vol. 32, No. 2, 01.04.2016, p. 344-357.

Research output: Contribution to journal › Article › peer-review

HarvardHarvard

Nikolopoulos, K, Buxton, S, Khammash, M & Stern, P 2016, 'Forecasting Branded and Generic Pharmaceuticals', International Journal of Forecasting, vol. 32, no. 2, pp. 344-357. https://doi.org/10.1016/j.ijforecast.2015.08.001

APA

Nikolopoulos, K., Buxton, S., Khammash, M., & Stern, P. (2016). Forecasting Branded and Generic Pharmaceuticals. International Journal of Forecasting, 32(2), 344-357. https://doi.org/10.1016/j.ijforecast.2015.08.001

CBE

Nikolopoulos K, Buxton S, Khammash M, Stern P. 2016. Forecasting Branded and Generic Pharmaceuticals. International Journal of Forecasting. 32(2):344-357. https://doi.org/10.1016/j.ijforecast.2015.08.001

MLA

Nikolopoulos, Kostas et al. "Forecasting Branded and Generic Pharmaceuticals". International Journal of Forecasting. 2016, 32(2). 344-357. https://doi.org/10.1016/j.ijforecast.2015.08.001

VancouverVancouver

Nikolopoulos K, Buxton S, Khammash M, Stern P. Forecasting Branded and Generic Pharmaceuticals. International Journal of Forecasting. 2016 Apr 1;32(2):344-357. Epub 2016 Jan 22. doi: 10.1016/j.ijforecast.2015.08.001

Author

Nikolopoulos, Kostas ; Buxton, Samantha ; Khammash, Marwan et al. / Forecasting Branded and Generic Pharmaceuticals. In: International Journal of Forecasting. 2016 ; Vol. 32, No. 2. pp. 344-357.

RIS

TY - JOUR

T1 - Forecasting Branded and Generic Pharmaceuticals

AU - Nikolopoulos, Kostas

AU - Buxton, Samantha

AU - Khammash, Marwan

AU - Stern, Philip

PY - 2016/4/1

Y1 - 2016/4/1

N2 - 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.

AB - 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.

U2 - 10.1016/j.ijforecast.2015.08.001

DO - 10.1016/j.ijforecast.2015.08.001

M3 - Article

VL - 32

SP - 344

EP - 357

JO - International Journal of Forecasting

JF - International Journal of Forecasting

SN - 0169-2070

IS - 2

ER -