Quant Mark Econ (2010) 8:429–460
`DOI 10.1007/s11129-010-9089-5
`
`Drivers of peak sales for pharmaceutical brands
`
`Marc Fischer & Peter S. H. Leeflang &
`Peter C. Verhoef
`
`Received: 20 January 2009 / Accepted: 28 July 2010 /
`Published online: 13 August 2010
`# The Author(s) 2010. This article is published with open access at Springerlink.com
`
`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
`
`M. Fischer (*)
`Business and Economics,
`University of Passau, Innstr. 27, 94032 Passau, Germany
`e-mail: marc.fischer@uni-passau.de
`P. S. H. Leeflang : P. C. Verhoef
`Economics and Business, University of Groningen, P.O. BOX 800, 9700 AV Groningen, Netherlands
`
`P. S. H. Leeflang
`e-mail: P.S.H.Leeflang@rug.nl
`
`P. C. Verhoef
`e-mail: p.c.verhoef@rug.nl
`
`P. S. H. Leeflang
`LUISS Guido Carli, Rome, Italy
`
`

`
`430
`
`1 Introduction
`
`M. Fischer et al.
`
`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 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
`
`

`
`Drivers of peak sales for pharmaceutical brands
`
`431
`
`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 Data-
`monitor 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.
`
`

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`432
`
`M. Fischer et al.
`
`2 Drug life cycles and peak sales
`
`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.
`
`2.2 Brand life cycles in drugs
`
`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.
`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.
`
`

`
`Drivers of peak sales for pharmaceutical brands
`
`433
`
`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
`
`Tellis et al.
`(2003)
`
`Time-to-
`takeoff
`
`Aggregated sales by
`category for
`different European
`countries
`
`Economic and cultural
`variables
`
`Time-to-
`takeoff
`
`Aggregated sales by
`category in the U.S.
`
`Price, new firm entry,
`commercialization year
`
`Agarwal
`and
`Bayus
`(2002)
`
`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
`
`- 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 sub-
`stantially across European
`countries.
`- Culture partially explains
`country differences.
`- 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.
`
`

`
`434
`
`Fig. 1 Illustration of peak sales
`in the French market for
`calcium channel blockers
`
`Time-to peak-sales
`Entrant 2
`
`Sales in mill.
`daily dosages
`
`365
`
`305
`
`245
`
`185
`
`125
`
`50
`
`M. Fischer et al.
`
`Height-of peak-sales
`Entrant 2
`
`1978
`
`1980
`
`1984
`1982
`Entrant 2
`
`1986
`
`1988
`1990
`Entrant 3
`
`1994
`1996
`1992
`Entrant 5
`
`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.
`
`

`
`Drivers of peak sales for pharmaceutical brands
`
`435
`
`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
`
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`M. Fischer et al.
`
`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, 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
`
`

`
`Drivers of peak sales for pharmaceutical brands
`
`437
`
`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
`
`

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`438
`
`M. Fischer et al.
`
`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 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 operationaliza-
`tion 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.
`
`4 Data
`
`4.1 Data sources
`
`We test our propositions using data from two prescription drug categories, calcium
`channel blockers and ACE inhibitors. These 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 expendi-
`tures 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
`
`

`
`Drivers of peak sales for pharmaceutical brands
`
`439
`
`Table 2 Variable definition and expected direction of effects
`
`Variable
`
`Definition
`
`Expected Effect on …
`
`Time-to-peak-
`sales
`
`Height-of-
`peak-sales
`
`−
`
`+/−
`
`+
`
`+/−
`
`−
`
`+
`
`N.A.
`
`+
`
`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
`
`MESt ¼P
`
`ð
`ÞrExptr;
`¼0 1 ϕ
`
`tr
`
`Order of entry
`
`Quality
`
`Order of entry ×
`quality
`Control variables
`Stock of own
`marketing
`expendituresa
`
`Stock of competitive
`marketing
`expenditures
`
`Number of
`Competitors
`
`where
`MESt = Stock of own marketing expenditures at the
`end of quarter t
`8 = (Estimated) quarterly depreciation rate
`Expt = Own marketing expenditures in quarter t
`See also Berndt et al. (1995, 102)
`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.
`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
`a To estimate the depreciation rate 8, 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 (1-8). 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.
`
`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
`expiry does not coincide with time-to-peak-sales.
`
`

`
`440
`
`M. Fischer et al.
`
`4.2 Measurement and descriptive statistics
`
`We adopt the slowdown measure by Golder and Tellis (2004) to determine each brand’s
`time of peak sales and cross-validate this decision through visual inspections of the
`time series. 45 brands out of 73 brands reach their peak sales during the 10-year
`observation period. A few brand time series are left-truncated because they were
`launched before the start of the observation period. Thus, we have an unbalanced panel
`data set with 31 quarters per brand, on average, which we use to calibrate our model.
`Our dataset shares a limitation with most previous studies on growth models. The
`data series are censored to the right. Hence, in a very strict sense, we cannot be
`absolutely sure that we observe the peak. It may just be a saddle phenomenon
`(Goldenberg et al. 2002), although that