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Conference Paper/Proceeding/Abstract 1132 views 107 downloads

Forecasting Pharmaceutical Life Cycles

Sam Buxton Orcid Logo, Kostas Nikolopoulos, Marwan Khammash, Philip Stern

Swansea University Author: Sam Buxton Orcid Logo

Abstract

This paper discusses the modelling and forecasting of pharmaceutical life cycles. Three different scenarios were found to exist when exploring the difference between the branded and generic life cycles. First after patent expiry, we examine the case where branded sales decline and the generic sales...

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Published: 2014
URI: https://cronfa.swan.ac.uk/Record/cronfa43655
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spelling 2018-10-22T12:16:46.2281330 v2 43655 2018-09-03 Forecasting Pharmaceutical Life Cycles 27aacc6d5049c8d2c26495e4e6a6bd75 0000-0003-1007-7063 Sam Buxton Sam Buxton true false 2018-09-03 BBU This paper discusses the modelling and forecasting of pharmaceutical life cycles. Three different scenarios were found to exist when exploring the difference between the branded and generic life cycles. First after patent expiry, we examine the case where branded sales decline and the generic sales increase (branded then generic), once the patent associated with the branded drug has expired. Then irrespective of patent expiration we examine two further cases. The first is where branded sales are high and generic sales are low (high branded, low generic) and the second is where branded sales are low and generic sales are high (high generic, low branded). Understanding the patterns of brand decline (and the associated generic growth) is increasingly important because in a market 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. The pharmaceutical prescription data comes from a database known as JIGSAW. The prescription drugs that were modelled were those that had the highest number of prescriptions within the database. Six methods were then used to model and forecast the life cycles of these drugs. The models used were: Bass Diffusion Model, Repeat Purchase Diffusion Model (RPDM), and Naïve with and without drift, Exponential Smoothing and Moving Average models. Based on previous research it was expected that the more complex models would produce more accurate forecasts for the branded and generic life cycles than the simple benchmark models. 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 for all three scenarios examined. Conference Paper/Proceeding/Abstract Forecasting; Diffusion Models; Pharmaceutical Lifecycles; Branded drugs; Generic drugs. 30 6 2014 2014-06-30 COLLEGE NANME Business COLLEGE CODE BBU Swansea University 2018-10-22T12:16:46.2281330 2018-09-03T11:05:19.2305045 Faculty of Humanities and Social Sciences School of Management - Business Management Sam Buxton 0000-0003-1007-7063 1 Kostas Nikolopoulos 2 Marwan Khammash 3 Philip Stern 4 0043655-22102018121609.pdf ISFconference2014.pdf 2018-10-22T12:16:09.9600000 Output 565301 application/pdf Author's Original true 2018-10-22T00:00:00.0000000 true eng
title Forecasting Pharmaceutical Life Cycles
spellingShingle Forecasting Pharmaceutical Life Cycles
Sam Buxton
title_short Forecasting Pharmaceutical Life Cycles
title_full Forecasting Pharmaceutical Life Cycles
title_fullStr Forecasting Pharmaceutical Life Cycles
title_full_unstemmed Forecasting Pharmaceutical Life Cycles
title_sort Forecasting Pharmaceutical Life Cycles
author_id_str_mv 27aacc6d5049c8d2c26495e4e6a6bd75
author_id_fullname_str_mv 27aacc6d5049c8d2c26495e4e6a6bd75_***_Sam Buxton
author Sam Buxton
author2 Sam Buxton
Kostas Nikolopoulos
Marwan Khammash
Philip Stern
format Conference Paper/Proceeding/Abstract
publishDate 2014
institution Swansea University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description This paper discusses the modelling and forecasting of pharmaceutical life cycles. Three different scenarios were found to exist when exploring the difference between the branded and generic life cycles. First after patent expiry, we examine the case where branded sales decline and the generic sales increase (branded then generic), once the patent associated with the branded drug has expired. Then irrespective of patent expiration we examine two further cases. The first is where branded sales are high and generic sales are low (high branded, low generic) and the second is where branded sales are low and generic sales are high (high generic, low branded). Understanding the patterns of brand decline (and the associated generic growth) is increasingly important because in a market 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. The pharmaceutical prescription data comes from a database known as JIGSAW. The prescription drugs that were modelled were those that had the highest number of prescriptions within the database. Six methods were then used to model and forecast the life cycles of these drugs. The models used were: Bass Diffusion Model, Repeat Purchase Diffusion Model (RPDM), and Naïve with and without drift, Exponential Smoothing and Moving Average models. Based on previous research it was expected that the more complex models would produce more accurate forecasts for the branded and generic life cycles than the simple benchmark models. 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 for all three scenarios examined.
published_date 2014-06-30T03:54:56Z
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score 11.037144

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