Drivers of peak sales for pharmaceutical brands - Springer
`
`http://link.springer.com/article/10.1007/s11129-010-9089-5/fulltext.html
`
`Quantitative Marketing and Economics
`QME
`© The Author(s) 2010
`10.1007/s11129-010-9089-5
`
`Marc Fischer 1 , Peter S. H. Leeflang 2, 3 and Peter C. Verhoef 2
`
`(1)
`(2)
`(3)
`
`Business and Economics, University of Passau, Innstr. 27, 94032 Passau, Germany
`Economics and Business, University of Groningen, P.O. BOX 800, 9700 AV Groningen, Netherlands
`LUISS Guido Carli, Rome, Italy
`
`Marc Fischer (Corresponding author)
`Email: marc.fischer@uni-passau.de
`
`Peter S. H. Leeflang
`Email: P.S.H.Leeflang@rug.nl
`
`Peter C. Verhoef
`Email: p.c.verhoef@rug.nl
`
`Received: 20 January 2009
`Accepted: 28 July 2010
`Published online: 13 August 2010
`
`Abstract
`
`Peak sales are an important metric in the pharmaceutical industry. Specifically, managers are focused on the height-of-peak-sales and the
`time required achieving peak sales. We analyze how order of entry and quality affect the level of peak sales and the time-to-peak-sales of
`pharmaceutical brands. We develop a growth model that includes these two variables as well as control variables for own and
`competitive marketing activities. We find that early entrants achieve peak sales later, and they have higher peak-sales levels.
`High-quality brands achieve peak sales earlier, and their peak-sales levels are higher. In addition, quality has a moderating effect on the
`order of entry effect on time-to-peak-sales. Our results indicate that late entrants have longer expected time-to-peak-sales when they
`introduce a brand with high quality.
`
`Keywords Peak-sales metrics – Brand growth – Econometric models – Market entry – Pharmaceutical marketing
`
`JEL Classification C23 – C51 – L65 – M31
`
`1 Introduction
`
`New products play a very important role in the pharmaceutical industry (Leenders and Wierenga 2008; Stremersch and van Dyck 2009).
`Pharmaceutical firms constantly introduce new drugs, and for these new drugs, peak sales represent an important metric, frequently used
`by investors to assess pharmaceutical firms’ value (Obeid and Vine 2005; Suresh et al. 2006).
`
`Peak sales can be characterized along two dimensions: the height-of-peak-sales and the time-to-peak-sales. Both metrics are closely
`related to new product performance such as cumulative sales. Intuitively, a brand with a higher level of peak sales is likely to have higher
`average sales over its life cycle. As a result, cumulative sales are also higher. Similarly, a brand with longer time-to-peak-sales enjoys a
`longer period of growth that contributes to accumulate sales and achieve a higher level of peak sales. Consequently, cumulative sales are
`again higher.
`
`There are, however, exceptions to these rules. For example, a high level of peak sales may be achieved very fast. Although, we cannot
`rule out such a case theoretically, we do not believe it occurs often in reality because of restrictions to growth. Note that the growth rate
`
`1 of 30
`
`5/21/2015 6:16 PM
`
`ARGENTUM Exhibit 1189
`Argentum Pharmaceuticals LLC v. Research Corporation Technologies, Inc.
`IPR2016-00204
`
`00001
`
`

`
`Drivers of peak sales for pharmaceutical brands - Springer
`
`http://link.springer.com/article/10.1007/s11129-010-9089-5/fulltext.html
`
`needs to double if the same level of peak sales is to be achieved in half of the time. Firms can handle faster growth only up to a certain
`level due to supply and resource restrictions. In Appendix A, we also demonstrate that faster growth implies a higher variance of sales
`and therefore higher cash-flow volatility which is not desirable (Srivastava et al. 1998). Hence, even if demand might allow for a shorter
`time-to-peak sales, there are limits to growth from the supply side. The broad sample of new drugs that forms the basis of our empirical
`study supports our view. Time-to-peak-sales enhances height-of-peak sales and both peak-sales metrics increase cumulative brand sales.
`Together the two metrics explain more than 96% of observed variance in cumulative brand sales.
`
`Time-to-peak-sales and height-of-peak-sales provide two important yardsticks that are easy to evaluate and predict even before launch.
`Assume management wants to assess the sales potential of a new product two years prior to launch. Cumulative sales may be obtained
`from the life cycle curve. Predicting the lifetime and sales for all periods, however, requires much more information than predicting only
`two peak-sales metrics. It is much easier to reach a consensus estimate for time-to-peak-sales and height-of-peak-sales. For
`pharmaceuticals, as an example, management can triangle information on the population size, the incidence of a disease and the
`reachable market share for the new drug to obtain an estimate for the height-of-peak-sales. Management would certainly use information
`on competitive entries, order of entry, marketing investment, etc. to predict the peak-sales metrics. Our empirical analysis provides
`important insights into the relevance of these variables for peak sales. Importantly, the analysis also suggests that those variables do not
`provide explanatory power for cumulative sales beyond the two peak-sales metrics. Hence, peak-sales metrics cannot simply be
`substituted by other predictors.
`
`It is therefore not surprising that peak-sales metrics are widely adopted in practice, especially within the pharmaceutical industry. For
`example, Salix Pharmaceuticals reportedly has tumbled because its IBD franchise will “only achieve peak sales of $ 99 million, lower
`than its previous estimate of $ 135 million because of the Dec. 28 approval of three generic balsalazide (Colazal) formulations by the
`Food and Drug Administration” (Trading Markets.Com 2007). The importance of peak-sales metrics prompts business intelligence
`agencies such as IMS or Datamonitor to predict the peak sales of newly introduced drugs (e.g., Datamonitor 2007). Not only are the
`metrics important in practice (Obeid and Vine 2005), but scholars also acknowledge their relevance. For example, Bauer and Fischer
`(2000) and Schmid and Smith (2002) demonstrate that time-to- and height-of-peak-sales differ across brands introduced into different
`drug categories and countries due to factors such as order of entry.
`
`In the marketing literature, there has been extensive attention to the role of order of entry and quality with regard to (new product)
`performance (Gielens and Dekimpe 2001; Kalyanaram and Urban 1992; Kalyanaraman and Wittink 1994; Robinson and Fornell 1985;
`Shankar et al. 1998). An important debate addresses the question whether first movers really have a competitive advantage as is often
`attributed to them (Golder and Tellis 1993; Kornelis et al. 2008; Zhang and Narasimhan 2000). Quite in contrast, recently Tellis and
`Johnson (2007) argue that delivering superior quality is considered as the most important driver of new product success. Hence, we
`assess the role of order of entry and quality for the market performance of a new drug in terms of peak sales.
`
`The main objective of this study is to determine the drivers of height-of peak-sales and time-to-peak-sales in the pharmaceutical industry.
`Through this study we contribute to the literature on new products and brand life cycles (Hauser et al. 2006), as this is the first study to
`investigate drivers of time-to- and height-of-peak-sales. We show that some drivers differentially impact height-of-peak-sales and
`time-to-peak-sales. For example, marketing expenditures increase the level of peak sales, while they decrease the time-to-peak-sales. We
`aim to contribute to the literature on drivers of new product performance with a further investigation on the relative roles of order of
`entry, quality, and marketing efforts (see Tellis and Johnson 2007). Importantly, our results suggest that quality has by far the strongest
`positive effect on height-of-peak-sales, while it reduces the time-to-peak-sales. Finally, by executing this study in the pharmaceutical
`industry we also contribute to existing knowledge on pharmaceutical marketing (e.g. Kremer et al. 2008; Stremersch and van Dyck
`2009).
`
`The article is organized as follows: In the next section, we review the literature on drug life cycles and provide explanations why peak
`sales is a quite common phenomenon in the evolution of drug sales. Subsequently, we discuss potential drivers of time-to- and height-
`of-peak-sales and how they might affect the two metrics. We develop a model to measure the effects, then describe data from the
`pharmaceutical industry and discuss estimation issues. We follow up with a discussion of the empirical results. We continue with a cross-
`sectional analysis of new product performance to substantiate the relevance of the suggested metrics. In the final section, we conclude
`with research implications, limitations, and suggestions for further research.
`
`2 Drug life cycles and peak sales
`
`2 of 30
`
`5/21/2015 6:16 PM
`
`00002
`
`

`
`Drivers of peak sales for pharmaceutical brands - Springer
`
`http://link.springer.com/article/10.1007/s11129-010-9089-5/fulltext.html
`
`2.1 Life cycle research
`
`Studying the length and the shape of brand life cycles has a long history, including studies by Bauer and Fischer (2000), Brockhoff
`(1967), and Polli and Cook (1969). Research on specific metrics at the brand level in the development of the brand life cycle is scarce.
`Height-of-peak-sales and time-to-peak-sales have not been studied so far. Remarkably, studying the diffusion of article citations in
`Econometrica and the Journal of Econometrics, Fok and Franses (2007) model the time-to-peak citations and peak-citations of an article.
`Hence, though studied in a different context, there is academic attention for peak metrics in the econometric diffusion literature. We
`believe it is important to study such metrics in a new product context as well, as we will show that both these metrics are highly relevant
`for practice and they both are the most important determinants of cumulative brand sales.
`
`Research at the product level has, however, investigated other specific metrics, as overviewed in Table 1. Several studies consider
`time-to-takeoff and subsequent growth of consumer durables (i.e., VCRs, televisions), as well as their drivers (Agarwal and Bayus 2002;
`Neelameghan and Chintagunta 2004; Golder and Tellis 1997; Stremersch and Tellis 2004; Tellis et al. 2003). Bayus (1998) specifically
`analyzes product lifetime as an important metric. Although these studies provide insights into which factors (i.e., economic, cultural)
`influence time-to-takeoff at the product level by country, they do not clarify the drivers at the brand level.
`
`3 of 30
`
`5/21/2015 6:16 PM
`
`00003
`
`

`
`Drivers of peak sales for pharmaceutical brands - Springer
`
`http://link.springer.com/article/10.1007/s11129-010-9089-5/fulltext.html
`
`Table 1
`Overview of prior studies investigating specific metrics over a brand’s/product’s life cycle
`
`Study
`
`Metrics
`
`Data
`
`Antecedents
`
`Major findings
`
`Golder and
`Tellis (1997)
`
`Time-to-takeoff
`
`Aggregated sales by category
`in the U.S.
`
`Price, year of introduction, market
`penetration
`
`Bayus (1998)
`
`Product lifetime
`
`Aggregated sales at various
`product market levels in the
`U.S. PC industry
`
`Product introduction year, firm entry
`year, technology substitution
`
`- Average time-to-takeoff is six years.
`
`- At takeoff, price is 63% of
`introductory price, and penetration is
`1.7%.
`
`- Time-to-takeoff decreases after
`World War II.
`
`- Product life cycles are not shrinking
`over time.
`
`- Observed acceleration in life cycles
`is a result of technology substitution.
`
`- Time-to-takeoff varies substantially
`across European countries.
`
`Tellis et al.
`(2003)
`
`Time-to-takeoff
`
`Aggregated sales by category
`for different European
`countries
`
`Economic and cultural variables
`
`- Culture partially explains country
`differences.
`
`Agarwal and
`Bayus (2002)
`
`Time-to-takeoff
`
`Aggregated sales by category
`in the U.S.
`
`Price, new firm entry, commercialization
`year
`
`Stremersch
`and Tellis
`(2004)
`
`Rate of growth,
`duration of growth
`
`Aggregated sales by category
`for different European
`countries
`
`Economic and cultural variables
`
`Golder and
`Tellis (2004)
`
`Time-to-takeoff,
`slowdown, duration
`of growth
`
`Aggregated sales by category
`in the U.S.
`
`Price, economic growth, type of product,
`market penetration
`
`This Study
`
`Time-to-peak-sales,
`height-of-peak-sales
`
`Brand sales of 45
`pharmaceutical brands in
`France, Germany, Italy, and
`UK
`
`Own marketing expenditures,
`competitive marketing expenditures,
`order-of-entry, quality, number of
`competitors
`
`2.2 Brand life cycles in drugs
`
`- Advantages for waterfall strategy for
`international product introduction.
`
`- Takeoff in new firm entry leads to
`sales takeoff.
`
`- Firm entry dominates other drivers of
`time-to-takeoff.
`
`- Growth metrics vary substantially
`across European countries.
`
`- Economic factors primarily explain
`country differences.
`
`- Slowdown occurs when sales
`declines by about 15%.
`
`- Probability of slowdown is higher
`when economic growth is slower, price
`reductions are smaller, and penetration
`is higher.
`
`- Leisure-enhancing products tend to
`have higher growth rates and shorter
`growth stages.
`
`- Order of entry reduces time-to-
`peak-sales and height-of-peak-sales.
`
`- Quality reduces time-to-peak-sales
`and increases height-of peak sales.
`
`- A higher quality reduces the negative
`effect of order of entry on time-to-
`peak-sales.
`
`4 of 30
`
`5/21/2015 6:16 PM
`
`00004
`
`

`
`Drivers of peak sales for pharmaceutical brands - Springer
`
`http://link.springer.com/article/10.1007/s11129-010-9089-5/fulltext.html
`
`The development of demand for a new drug derives from both adoptions and repeats. For many drugs, the evolution of sales shows a
`peak. In Fig. 1, we depict the sales development of different brands in the French calcium channel blockers market. On the X-axis, we
`display the launch years of new drugs, and on the Y-axis, we provide annual sales for three entrants in this market. The figure clearly
`shows the occurrence of sales peaks. For example, the second entrant reaches its peak in 1988 with a sales level of approximately 200
`million daily dosages.
`
`Fig. 1
`Illustration of peak sales in the French market for calcium channel blockers
`
`The theories of adoption and imitation (Bass 1969) and informational cascades (Golder and Tellis 2004) explain why brand sales follows
`a life cycle. However, they are usually associated with the product level and first-time adoptions. Brand sales are, in addition, composed
`of repeat purchases, and they are subject to competition (Hahn et al. 1994).
`
`Although some authors have questioned the transfer of the product life cycle concept to brands (Dhalla and Yuspeh 1976), brand life
`cycles have been reported quite frequently. The broadest evidence for brand life cycles is available for pharmaceuticals. Bauer and
`Fischer (2000), Corstjens et al. (2005), Cox (1967), Grabowski and Vernon (1990), Hahn et al. (1994), Lilien et al. (1981), Rao and
`Masataka (1988), and Simon (1979) all find strong evidence for the existence of a drug life cycle. In total, these researchers document
`the life cycles for more than 500 newly introduced drugs.
`
`There are potentially several reasons why especially pharmaceuticals exhibit a peak in their sales trajectory. First, diffusion dynamics
`seem to be dominant for the evolution of (prescription) drug sales. Although refills have a large share in total drug sales, sales dynamics
`are predominantly driven by first-time prescriptions. This is simply due to the fact that physicians are reluctant to change a drug (i.e. the
`first-time prescription) once it has been found to work for a patient, even in response to heavy marketing initiatives by competitor
`brands. As a result, researchers have adopted diffusion approaches to model drug sales with repeats where repeat rates are assumed
`constant (e.g., Hahn et al. 1994; Shankar et al. 1998). Second, pharmaceutical companies concentrate the bulk of their marketing efforts
`(i.e., detailing) on the first two years after launch, which causes an immediate strong increase in prescriptions but results in slower sales
`growth or even decline in later years when marketing support is only limited (Osinga et al. 2010). Third, by definition there is a limit in
`users of a drug as it is only relevant for patients with a specific treatment for a disease. Fourth, the entry of new competitors may inhibit
`sales growth to continue. These competitors may be other innovative drugs within the same or new categories, or generics after the
`patent has expired.
`
`One might argue that instead of studying time-to- and height-of-peak-sales, one should study the time-to-takeoff metric (e.g. Tellis et al.
`2003). As noted, this metric has been studied at the category level but not at the brand level. So far, there is no empirical evidence in the
`literature that this phenomenon occurs at the brand level, as well. We explored the available brand time-series but did not find evidence
`for a sales-takeoff phenomenon. Hence, beyond our substantive arguments there are also empirical arguments to study peak-sales
`metrics.
`
`5 of 30
`
`5/21/2015 6:16 PM
`
`00005
`
`

`
`Drivers of peak sales for pharmaceutical brands - Springer
`
`http://link.springer.com/article/10.1007/s11129-010-9089-5/fulltext.html
`
`3 Variables affecting peak sales of pharmaceutical brands
`
`3.1 Potential peak-sales drivers
`
`In this study, we focus on the impact of order of entry and quality on (1) the time-to-peak-sales and (2) the height-of-peak-sales. We
`control for the effect of other, potentially relevant variables. Specifically, we consider own and competitive marketing support, the
`number of competitive entries into the market, the price of the drug, and marketing expenditures by co-marketing partners as control
`variables in the proposed brand sales model.
`
`We define a pharmaceutical brand as a new chemical entity (NCE) marketed by a company in a specific country. Typically, innovative
`drugs enjoy patent protection for several years after launch. The competitive situation changes dramatically for an innovative drug when
`its patent expires and cheaper generic competitors rush into the market. In addition, innovative brands may be attacked by brands from
`other categories that offer alternative forms of drug treatment (technological substitution). Given that our data come from two categories
`that belong to the most innovative categories without generic price competition in our observation period, we focus on effects from
`competition among innovative prescription drugs within a category.
`
`The sales evolution, and thus our focal metrics time-to-peak-sales and height-of-peak-sales, is determined by four key metrics: the speed
`of adoption, the size of the adopter (patient) potential, the repurchase (refill) rate and the interpurchase time. For the majority of
`categories, repeat sales dynamics (interpurchase time and repurchase/refill rate) are determined by exogenous factors such as therapy
`guidelines, patient characteristics, and physician skills. Physicians rarely risk switching the patient to another drug in response to
`marketing activities, because effectiveness and compatibility of the new drug are uncertain to a patient. Additionally, the time between
`refills follows medical needs for optimal treatment. As a consequence, we focus on the diffusion metrics speed of adoption and size of
`adopter potential in our discussion of order-of-entry and quality effects on peak sales as these decision variables do not change the repeat
`sales dynamics. Aggregate repeat sales metrics are constant over the life cycle of a drug unless therapy guidelines are replaced or the
`distributions of patient and physician characteristics change systematically (Hahn et al. 1994).
`
`3.2 Effects of order of entry and quality
`
`3.2.1 Order of entry
`
`Multiple studies investigate the impact of order of entry on a brand’s market share and sales (e.g., Kalyanaram et al. 1995; Robinson and
`Fornell 1985; Urban et al. 1986). Late entrants grow faster but achieve lower market share levels (Kalyanaram and Urban 1992). The
`effect has also been demonstrated for pharmaceuticals (Berndt et al. 1995; Shankar et al. 1998). These results are due to the fact that
`late-mover drugs face a lower adopter potential that is faster penetrated. In addition, late entrants are less effective than early entrants in
`competing for the remaining potential adopters (Kalyanaram and Urban 1992; Lieberman and Montgomery 1988). As a result, late
`entrants expect a lower level of peak sales and a shorter time-to-peak-sales. We note, however, that this effect may be moderated by other
`variables such as quality.
`
`If late entrants penetrate the expected lower adopter potential at the same speed with similar support and quality as earlier entrants,
`time-to-peak-sales will be shorter. There are, however, good reasons to believe that their adoption speed is even faster than for early
`entrants. This is because buying resistance at the category level is much lower than it had been in earlier periods of the market life cycle.
`With more brands, consumers have had multiple opportunities to collect consumption experiences. Furthermore, social pressures on later
`adopters may favor trial probabilities for late mover brands (Bass 1969; Rogers 1995). The entry of another brand should not only
`increase brand variety but also enhance marketing activity in terms of pricing, advertising and promotion, which may lead to faster
`adoptions (Horsky and Simon 1983; Krishnan et al. 2000; Prins and Verhoef 2007). To summarize, we expect order of entry to reduce
`both the time-to-peak-sales and the height-of-peak-sales.
`
`3.2.2 Quality
`
`Quality is considered of essential importance for new product success (Tellis and Johnson 2007). There is no doubt that a better quality
`increases the attractiveness of the brand, which in turn should enlarge its adopter potential. Indeed, many studies demonstrate the
`positive sales effect of quality for drugs (Berndt et al. 1995). Hence, we expect a positive relation between the level of quality and the
`level of peak sales.
`
`Adoption theory predicts that adoption will be faster for products with a relative quality advantage (Rogers 1995). Consequently,
`
`6 of 30
`
`5/21/2015 6:16 PM
`
`00006
`
`

`
`Drivers of peak sales for pharmaceutical brands - Springer
`
`http://link.springer.com/article/10.1007/s11129-010-9089-5/fulltext.html
`
`high-quality brands should face lower adoption barriers leading to a shorter time-to-peak-sales. The expected time-to-peak-sales should
`therefore decrease with quality. However, we have also argued that a better quality enlarges the adopter potential leading to a potentially
`longer time-to-peak-sales. If this effect is stronger than the accelerated adoption (trial) effect, we would expect a longer time-to-
`peak-sales for high-quality brands. As a consequence, we cannot provide a clear prediction about the direction of the (direct) quality
`effect and consider it as an empirical issue.
`
`3.2.3 Interaction between order of entry and quality
`
`Prior research has shown that the innovativeness of a new drug can reduce the disadvantage from being a late entrant (Shankar et al.
`1998). The study of Shankar et al., however, focused on market share or brand sales, but not on peak-sales metrics. Hence, we cannot
`make any inference about the interaction effect between quality and order-of-entry with respect to the time-to-peak-sales. For time-to-
`peak-sales, we expect that a higher quality reduces the negative effect of a late entry on the time-to-peak-sales. A product with better
`quality may provide the late entrant with more opportunities to gain prescriptions from competing incumbent brands. Low-quality late
`entrants won’t be able to attract these prescriptions. As a consequence, the adopter potential for low-quality entrants will be lower and
`they achieve their peak-sales earlier than high-quality late entrants. Quality thus reduces the negative impact of order of entry on time-to-
`peak-sales, i.e. we expect a positive interaction effect between quality and order of entry.
`
`3.3 Control variables
`
`3.3.1 Own marketing support
`
`The amount of marketing support will affect the time-to- and height-of-peak-sales. Prior research shows that higher marketing
`expenditures speed up adoption (Gielens and Steenkamp 2007; Prins and Verhoef 2007; Steenkamp and Gielens 2003). Diffusion
`research demonstrates that higher marketing expenditures increase the diffusion rate and extend the market potential of adopters (Horsky
`and Simon 1983; Mahajan et al. 1990). Depending on which of these two effects is larger, the time-to-peak-sales will be shortened or
`extended. A recent meta-analysis (Kremer et al. 2008) shows that own marketing expenditures improve the sales base of a drug. Hence,
`we expect a positive effect of own marketing support on the height-of-peak-sales.
`
`3.3.2 Competitive marketing support
`
`Initially, we might predict that competitive marketing expenditures negatively affect the time-to-peak-sales. Prins and Verhoef (2007)
`show that competitive advertising reduces time to adoption. But competitive marketing efforts may also have a category-building effect
`that enhances both own and competitors’ brand sales (Dubé and Manchanda 2005; Fischer and Albers 2010) and may extend the time-to-
`peak-sales. As a consequence, it is difficult to provide a clear prediction about the effect of competitive marketing expenditures on
`time-to-peak-sales.
`
`3.3.3 Number of competitors
`
`The number of competitors in the market might also be relevant in explaining both time-to and height-of-peak-sales. The market share
`theorem (Cooper and Nakanishi 1988) postulates that a brand’s market share is inversely related to the number of competitors in a
`market. The brand looses in relative attractiveness. The loss in attractiveness may have different sources. The memory-sequence effect
`(Alpert and Kamins 1994) or the variety seeking phenomenon (McAlister and Pessemier 1982) may explain the loss in attractiveness
`when more competitors are present in a market. We expect that the number of competitors reduces both the time-to-peak-sales and the
`height-of-peak-sales.
`
`3.3.4 Price
`
`In the empirical application, we use a sample of firms and brands from four European countries (France, Germany, Italy, and the U.K.)
`during the period 1987–1996. The systems of health insurance and price regulation in these countries explain why pharmaceutical firms
`did not use price as a tactical marketing instrument in that period. In France and Italy, prices are determined exogenously by the
`respective authorities. In principle, launch prices can be set freely in the U.K. and Germany. Because drug expenses are typically
`reimbursed, demand is inelastic. As a result, firms have no incentive to reduce the price over time. Since a subsequent increase of the
`approved launch price would result in the loss of the reimbursement status, prices are also not raised in later periods and thus stay rather
`constant. Hence, price plays more or less the role of a cross-sectional control variable in this specific sample. Note also that we do not
`
`7 of 30
`
`5/21/2015 6:16 PM
`
`00007
`
`

`
`Drivers of peak sales for pharmaceutical brands - Springer
`
`http://link.springer.com/article/10.1007/s11129-010-9089-5/fulltext.html
`
`observe generic price competition in the sample.
`
`3.4 Summary
`
`We provide a summary of the expected effects of our included drivers of time-to- and height-of-peak-sales in Table 2. In this table, we
`also report the operationalization of these variables, which we discuss in the next section in more detail. Finally, we note that we do not
`include distribution as predictor in our model (Kremer et al. 2008; Manchanda et al. 2005), as drug distribution in Europe is highly
`regulated, such that pharmacies are required by law to supply all approved prescription drugs.
`
`Table 2
`Variable definition and expected direction of effects
`
`Variable
`
`Definition
`
`Order of entry
`
`Count variable that counts the entry of NCEs into a product market
`
`Objective quality index based on evaluations of drug quality in international meta-analytic
`review reports (see Appendix B for details)
`
`See above
`
`
`ME =
`
`St ∑tr=0 (1 − φ)r
`
`Ex
`
`pt−r
`
`,
`
`where
`
`Quality
`
`Order of entry ×
`quality
`
`Control variables
`
`Stock of own
`marketing
`expendituresa
`
`Expected Effect on …
`
`Time-to-
`peak-sales
`
`Height-
`of-peak-sales
`
`−
`
`+/−
`
`+
`
`−
`
`+
`
`N.A.
`
`MES t = Stock of own marketing expenditures at the end of quarter t
`
`+/−
`
`+
`
`φ = (Estimated) quarterly depreciation rate
`
`Exp t = Own marketing expenditures in quarter t
`
`See also Berndt et al. (1995, 102)
`
`Stock of competitive
`marketing
`expenditures
`
`Expenditures of all other brands (excluding co-marketing partners) in a product market are
`cumulated to produce competitive marketing expenditures. The calculation of the stock follows
`the approach as outlined under own marketing expenditures.
`
`Number of
`Competitors
`
`Count variable that counts the number of NCEs that have entered a product market in a specific
`quarter
`
`+/−
`
`−
`
`+/−
`
`N.A.
`
`N.A.—Not estimated due to collinearity issues or data limitations
`aTo estimate the depreciation rate φ, we replace the stock variables in the brand sales model (Eq. 3) by quarterly marketing expenditures and add
`lagged sales to the predictor variables. The coefficient associated with the lagged dependent variable measures the carryover-coefficient (1-φ). To
`control for brand heterogeneity in this model, we specify a random brand constant that follows a normal distribution and estimate its mean and
`variance.
`
`4 Data
`
`4.1 Data sources
`
`We test our propositions using data from two prescription drug categories, calcium channel blockers and ACE inhibitors. These
`
`8 of 30
`
`5/21/2015 6:16 PM
`
`00008
`
`

`
`Drivers of peak sales for pharmaceutical brands - Springer
`
`http://link.springer.com/article/10.1007/s11129-010-9089-5/fulltext.html
`
`categories differ in their therapeutic principles for treating diseases such as hypertension or coronary heart disease. Data collected by
`IMS Health are available on a quarterly basis for a time period of 10 years (1987–1996) in each country. During that period, calcium
`channel blockers and ACE inhibitors represented the two largest (in monetary value) categories of cardio-vascular therapy. They offered
`the most advanced therapy alternatives for the treatment of hypertension, as an example, and substituted sales from established older
`categories such as diuretics or beta blockers. Lipid modifying agents (e.g., Lipitor) and A-II-inhibitors (e.g., Cozaar), which lead today’s
`cardio-vascular drug prescriptions, were only available at the very end of the observation period.
`
`The data include normalized (across different application forms) unit sales (transformed into daily dosages by a brand-specific dosage
`factor), daily dosage price and marketing spending, including expenditures on detailing (>90% of total spending), professional journal
`advertising and direct mailings. Sampling expenditures and other below-the-line activities (e.g., dinner invitations for physicians) are not
`covered. The data come from four European countries—France, Germany, Italy, and the United Kingdom—and comprise 73 brands in
`eight product markets (2 categories × 4 countries). In addition, the data provide the month of product launch, a quality measure for each
`NCE and marketing expenditures by competitors and firms co-marketing the product, a frequently used method in this industry to
`enhance the diffusion of a new drug among physicians.
`
`Both categories experienced multiple brand entries during the study period. The first calcium channel blocker was introduced in
`Germany in 1963 by Knoll, a German company which has been acquired by Abbott. The first ACE inhibitor was introduced in the
`United States and other countries in 1981 by Bristol Myers-Squibb. Additional countries followed quickly in both categories. Between 9
`and 12 brands entered the country markets in each category, with most entries occurring during our observation period. Both categories
`experienced considerable growth in the 1980s, which slowed down or even turned into negative growth by 1996. The categories are
`comparable in their sales histories.
`
`Even though the patent expired for a few early entrants in the calcium channel blocker category in the 1980s, intense generic competition
`did not evolve during our observed period. This is mainly due to the fact that cost containment issues did not play an important role in
`prescription behavior at that time. The market share of the few generic competitors is on average below 1%. In addition, we observe that
`all innovative brands continued to grow after they lost patent protection, i.e. patent