Forecasting pharmaceutical life cycles: a case study on how drugs are prescribed in the NHS in the UK

Buxton, Samantha, Khammash, Marv, Nikopoulos, Konstantinos and Stern, Philip (2013) Forecasting pharmaceutical life cycles: a case study on how drugs are prescribed in the NHS in the UK. In: 2013 International DSI (Decision Sciences Institute) and Asia Pacific DSI Conference, 9-13 July 2013, Bali, Indonesia.

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Abstract

This paper is a case study on how pharmaceuticals are prescribed on the NHS in the UK. The paper discusses the modelling and forecasting of pharmaceutical life cycles, specifically around after the time of patent expiry. In this situation one of two things can occur the branded pharmaceutical sales remain high while the generic are low, the alternative is when the branded drug declines and stays low while the sales of the generic drug are high.. Understanding the patterns of brand decline (and the associated generic growth) is increasingly important because in a market currently worth over £7bn in the UK, the number of new ‘blockbuster’ drugs continues to decline. As a result pharmaceutical companies make efforts to extend the commercial life of their brands, and the ability to forecast is important in this regard. Second, this paper provides insights for effective governance because the use of a branded drug (when a generic is available) results in wasted resources. Five methods are used to model and forecast these life cycles: Bass Diffusion, Repeat Purchase Diffusion Model (RPDM), Naïve, Exponential Smoothing and Moving Averages. The empirical evidence presented here suggests that the use of the Naïve model incorporating drift provided the most accurate and robust method of modelling both types of prescribing, with the more advanced models being less accurate.

Item Type: Conference or Workshop Item (Paper)
Schools and Departments: School of Business, Management and Economics > Business and Management
Subjects: H Social Sciences
Depositing User: Tahir Beydola
Date Deposited: 19 Sep 2016 16:10
Last Modified: 19 Sep 2016 16:10
URI: http://sro.sussex.ac.uk/id/eprint/63420

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