Applying the Bass model to pharmaceuticals in emerging markets

Porath, Daniel; Schaefer, Christian

doi:10.2501/ijmr-2014-033

Cited by 6 publications

(9 citation statements)

References 7 publications

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“…In addition, the imitation coefficient is relatively high, suggesting that the influence of verbal transmission is relatively large. As a rule of thumb, the average innovation coefficient p is about 4% and the average imitation coefficient q is about 45% in mature markets, with the innovation parameter being much lower than the imitation parameter (Porath and Schaefer, 2012).…”

Section: Resultsmentioning

confidence: 99%

“…While previous studies have justified applying the Bass model to study the diffusion of pharmaceutics (Desiraju et al , 2004; Vakratsas and Kolsarici, 2008; Porath and Schaefer, 2012), marketing expenditures, a traditional economic variable, has not been incorporated into the model to investigate how marketing affects sales growth in the pharmaceutical market. Ruiz-Conde et al (2014) analyze US data to investigate the impact of marketing interventions on diffusion, and they find that a reallocation of marketing budgets can help in attaining the saturation level faster.…”

Section: Literature Review and Hypothesesmentioning

confidence: 99%

“…Desiraju et al (2004) distinguish between developed and developing pharmaceutical markets and show heterogeneity in terms of market potential and diffusion speed. Extending the Bass model to emerging markets, Porath and Schaefer (2012) find heterogeneity between mature and emerging markets.…”

Section: Literature Review and Hypothesesmentioning

confidence: 99%

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Marketing investment and pharmaceutical product diffusion

Qian

Yin

2020

MIP

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PurposeThis paper aims to explore the influence of marketing investment on drug diffusion processes, to analyze the heterogeneity of the diffusion characteristics and to understand the drug diffusion patterns in the prescription and over-the-counter (OTC) markets.Design/methodology/approachThe study introduces marketing investment into the Bass model. The authors use the Generalized Bass Model (GBM) to examine the influence of marketing efforts on drug diffusion in Chinese prescription and OTC markets.FindingsThe results of this study suggest that the imitation effect in the prescription drug market is greater than that in the OTC drug market; drug diffusion in the OTC market reaches saturation earlier in the diffusion process. Before reaching the critical state, the effect of marketing investment on drug diffusion in the OTC market is greater than that in the prescription market, and after the critical state, drug diffusion in the prescription market is more sensitive to marketing investment.Originality/valueThe study demonstrates the value of the GBM in empirical analyses of drug diffusion across two distinct markets, and the marketing regulation policies governments adopt have a powerful impact on the speed at which drugs become available in different markets. It enriches the extant product diffusion literature by highlighting the different diffusion patterns of the two segments of pharmaceutical market.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Review and Hypothesesmentioning

confidence: 99%

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Marketing investment and pharmaceutical product diffusion

Qian

Yin

2020

MIP

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“…Turning to the specific application of diffusion of innovation models to the pharmaceutical market, we mention, among others, Hahn et al (1994) and, more recently, Nikolopoulos et al (2016). Johnson (2005) and Porath and Schaefer (2014) address specifically the issue of the BM applied to individual newly launched drugs and support the model's good performance from the predictive point of view.…”

Section: Ijphm 114mentioning

confidence: 99%

Pre-launch forecasting of a pharmaceutical drug

Guseo

Valle

Furlan

et al. 2017

IJPHM

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Purpose The emergence of a pharmaceutical drug as a late entrant in a homogeneous category is a relevant issue for strategy implementation in the pharmaceutical industry. This paper aims to suggest a methodology for making pre-launch forecasts with a complete lack of information for a late entrant. Design/methodology/approach The diffusion process of the emerging entrant is estimated using the diffusion dynamics of pre-existing drugs, after an appropriate assessment of the drug’s entrance point. The authors’ methodology is applied to study the late introduction of a pharmaceutical drug in Italy within the category of ranitidine. Historical data of seven already active drugs in the category are used to assess and estimate ex ante the dynamics of a late entrant (Ulkobrin). Findings The results of applying the procedure to the ranitidine market reveal a high degree of accuracy between the ex post observed values of the late entrant and its ex ante mean predicted trajectory. Moreover, the assessed launch date corresponds to the actual date. Research limitations/implications The category has to be homogeneous to ensure a high degree of similarity among the existing drugs and the late entrant. For this reason, radical innovations cannot be forecast with this methodology. Originality/value The proposed approach contributes to the still challenging research field of pre-launch forecasting by estimating the dynamic features of a homogeneous category and exploiting them for forecasting purposes.

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“…Incorporating these relationships via a mixed data model may be useful but most analyses of prescribing to date consider univariate responses 3,7–10 . In addition, prescribing rates often have complex patterns over time due to unknown marketing efforts, policy changes, different user or prescriber subpopulation 11 .…”

Section: Introductionmentioning

confidence: 99%

A flexible mixed‐data model applied to claims data for post‐market surveillance of prescription drug safety behavior

Butler

Rice

Carlson

et al. 2022

Pharmaceutical Statistics

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We develop a new modeling framework for jointly modeling first prescription times and the presence of risk-mitigating behavior for prescription drugs using real-world data. We are interested in active surveillance of clinical quality improvement programs, especially for drugs which enter the market under an FDA-mandated Risk Evaluation and Mitigation Strategy (REMS). Our modeling framework attempts to jointly model two important aspects of prescribing, the time between a drug's initial marketing and a patient's first prescription of that drug, and the presence of risk-mitigating behavior at the first prescription. First prescription times can be flexibly modeled as a mixture of component distributions to accommodate different subpopulations and allow the proportion of prescriptions that exhibit risk-mitigating behavior to change for each component. Risk-mitigating behavior is defined in the context of each drug. We develop a joint model using a mixture of positive unimodal distributions to model first prescription times, and a logistic regression model conditioned on component membership to model the presence of risk-mitigating behavior. We apply our model to two recently approved extended release/long-acting (ER/LA) opioids, which have an FDA-approved blueprint for best prescribing practices to inform our definition of risk-mitigating behavior. We also apply our methods to simulated data to evaluate their performance under various conditions such as clustering.

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Applying the Bass model to pharmaceuticals in emerging markets

Cited by 6 publications

References 7 publications

Marketing investment and pharmaceutical product diffusion

Marketing investment and pharmaceutical product diffusion

Pre-launch forecasting of a pharmaceutical drug

A flexible mixed‐data model applied to claims data for post‐market surveillance of prescription drug safety behavior

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