Do Pharmaceutical Sales Respond to
`Scientific Evidence?
`
`Pierre Azoulay
`Columbia University, New York, NY 10027-6902
`pierre.azoulay@columbia.edu
`
`I investigate how different sources of information influence the diffusion of
`pharmaceutical innovations. In prescription-drug markets, both advertising
`and scientific information stemming from clinical trials can affect physicians’
`prescription choices. Using novel indices of clinical-research output, I find
`that both marketing and scientific evidence directly influence the diffusion
`process in the antiulcer-drug market, with marketing having a more pro-
`nounced influence. I also find evidence that clinical outputs are important
`drivers of firms’ marketing efforts, affecting sales indirectly. Taken together,
`the direct and indirect effects of science on demand imply strong private
`incentives for clinical research. I conclude that product-market competition
`in the pharmaceutical industry is shaped by both advertising rivalries and
`scientific rivalries. Moreover, drug advertising may perform an important
`informative function.
`
`Introduction
`1.
`How do different types of information influence the diffusion of phar-
`maceutical innovation? The spread of technological advances is lim-
`ited by the extent to which relevant information is available among
`potential adopters. Furthermore, the information necessary for the dif-
`fusion of pioneer products may be different from that required for the
`market penetration of subsequent innovations.
`In most industries, one would expect underinvestment in the
`production of knowledge to limit the availability of objective sources
`of
`information about product characteristics, safety, and efficacy
`(Arrow, 1962). However, in prescription-drug markets, two features
`of the institutional environment—extensive, government-mandated
`
`For useful suggestions and support, I would like to thank audience participants at
`the MIT IO Lunch and the NBER Productivity Lunch, as well as Dan Ackerberg,
`Richard Caves, Peter Davis, John DeFigueiredo, Sara Ellison, Stan Finkelstein, Jeff Fur-
`man, David Genesove, Jerry Hausman, David Hsu, Rebecca Henderson, Kip King, Bob
`Pindyck, Robert Rubin, Otto Toivanen, and especially Scott Stern and Ernie Berndt. The
`usual disclaimer applies.
`
`© 2002 Massachusetts Institute of Technology.
`Journal of Economics & Management Strategy, Volume 11, Number 4, Winter 2002, 551–594
`
`ALCON 2021
`Apotex Corp. v. Alcon Research, Ltd.
`Case IPR2013-00428
`
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`552
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`Journal of Economics & Management Strategy
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`testing requirements, and the structure of incentives in academic
`medicine—provide a context in which privately valuable information
`is made publicly available through the publication of clinical studies
`in medical journals. In addition, pharmaceutical companies promote
`their products extensively,
`though disagreements remain among
`economists and policy makers concerning the role of drug adver-
`tising. For some, marketing activities foster the rapid dissemination
`of product information about potentially life-saving products, while
`others emphasize its strategic use by sellers of incumbent brands to
`jam information channels that could be used by new entrants (Leffler,
`1981; Hurwitz and Caves, 1988).
`A finding that pharmaceutical sales do not respond to scien-
`tific information (holding advertising intensity constant) would be
`consistent with the jamming hypothesis. In contrast, a positive sci-
`ence elasticity of demand would imply that a more nuanced view
`of the relationships between advertising, scientific information, and
`demand is needed. Moreover, boundaries between science and adver-
`tising in pharmaceutical markets are blurry, since much advertising
`refers explicitly to clinical results. Thus, the pharmaceutical indus-
`try provides a unusual setting in which to compare the informative
`as well as persuasive functions of advertising: Are firms’ promotion
`efforts sensitive to changes in the supply of objective, scientific infor-
`mation contained in published clinical studies?
`I explore these questions using data pertaining to a partic-
`ular subset of the antiulcer-drug market: the therapeutic class of
`histamine2-receptor antagonists, commonly referred to as H2 antago-
`nists or simply H2 blockers. It enjoyed explosive growth from 1977,
`the year of the pioneer drug’s introduction, until the early 1990s,
`when there were four related molecules in this class vying for mar-
`ket dominance.1
`Importantly, product-market competition in this
`therapeutic market was marked by the overthrow of an established
`monopolist (Tagamet) by a subsequent entrant (Zantac). As noted by
`Suslow (1997), this change in market dominance could be the result
`of intense price competition, advertising rivalry (both persuasive
`and informative), or a battle to offer the most attractive package of
`nonprice attributes. In this paper, I argue that among these nonprice
`attributes, published clinical results contributed significantly to this
`turnover in market leadership.
`
`1(cid:1) During the time spanned by the dataset, none of these drugs went off patent or
`moved to the over-the-counter (OTC) market. Therefore, I can safely ignore important
`issues such as substitution with generics and market segmentation between distribution
`channels.
`
`

`

`Do Pharmaceutical Sales Respond to Scientific Evidence?
`
`553
`
`Using a brand-level, discrete-choice model of product differen-
`tiation, I examine the impact of scientific information embodied in
`randomized controlled trials (RCTs) on the sales of these four drugs.
`I attempt to use the fact that RCTs can use either a placebo or a com-
`peting drug as a control group to isolate the effects of these two
`types of scientific information on drug sales, contingent on market
`structure. The results show that both marketing and science directly
`influence the diffusion process, with marketing having a more pro-
`nounced influence. I also examine the possibility of an indirect influ-
`ence of scientific information on demand by estimating advertising
`response functions, and I find some evidence that clinical-research
`outputs indeed drive firms’ marketing expenditures. Plugging back
`the advertising equation into the demand system, the sum of the
`direct and indirect effects yields total demand elasticities of science
`of between 0.3 and 0.5 for the pioneer drug and its challenger.
`Overall,
`these results are consistent with a view that sees
`product-market competition outcomes in the pharmaceutical industry
`as the result of firms’ rivalrous efforts in marketing and applied sci-
`ence. They cast doubt on the validity of the belief, widespread in the
`medical community, that drug advertising totally jams other conduits
`of professionally sanctioned information, such as the results of RCTs
`(Wade et al., 1989). Finally, these findings help explain the grow-
`ing involvement of industry in the conduct and funding of clinical
`research. Not only do clinical expenditures contribute to meet safety
`and efficacy requirements (thereby securing regulatory approval for
`entry), they also constitute investments marked by long-lived and
`direct economic payoffs on the product market.
`The remainder of the paper proceeds as follows. Section 2
`reviews the literature on drug advertising and the diffusion of phar-
`maceutical innovations. Section 3 provides a short background on
`the antiulcer-drug market, in addition to describing the dataset and
`constructing clinical-output variables. Section 4 presents the econo-
`metric results for the discrete-choice model, while Section 5 provides
`estimates of advertising response functions. I offer some concluding
`remarks and suggestions for future research in Section 6.
`
`2. Literature Review
`The diffusion of pharmaceutical
`innovations is a complex social
`process and is subject to multiple influences. Because drugs are expe-
`rience goods, the impact of entry is limited by physicians’ switching
`costs and herding around the most popular products in a given
`therapeutic class (Coscelli, 2000; Berndt et al., 2000). As a result,
`
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`Journal of Economics & Management Strategy
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`diffusion is rooted in learning, word-of-mouth, and other dynamic
`phenomena occurring within the population of potential adopters. In
`their landmark study of tetracycline’s diffusion, Coleman et al. (1966)
`emphasized these demand-pull forces by documenting the heterogene-
`ity of the physician population with regard to patterns of information
`consumption, and highlighted the role of “medical opinion leaders”
`who were both among the early adopters of this novel antibiotic
`and closely tied with the academic medical community. On the other
`hand, diffusion paths are also influenced by technology-push forces,
`in particular the approval by the Food and Drug Administration of
`additional indications for existing drugs (or of additional therapies
`within a given therapeutic market). These decisions result in the
`fall of quality-adjusted prices over time, triggering the adoption of
`inframarginal consumers.
`While there exists numerous sources of information that might
`influence the adoption of pharmaceutical innovations at the individ-
`ual physician level, at a more aggregate level information regarding
`product quality is made available to potential adopters through two
`primary information channels: advertising by pharmaceutical firms
`and published clinical results regarding the safety and efficacy of drug
`therapies.2
`Beginning with Bond and Lean’s (1977) FTC study, economists
`have extensively studied the role of drug advertising. In experience-
`goods markets, the mere fact that a product is advertised can signal
`to customers that it is of high quality (Nelson, 1974; Milgrom and
`Roberts, 1986). In this perspective, advertising can be interpreted
`as performing mostly a persuasive role, since it conveys informa-
`tion only implicitly. The medical literature has further argued that
`advertising swamps the effect of professionally sanctioned sources
`of information (Avorn et al., 1982; Manning and Denson, 1980) and
`has deleterious effects on medical practice (Wade et al., 1989). Phar-
`maceutical firms promote their products heavily, with advertising
`expenditures typically amounting to between 12% and 15% of sales.
`The most heavily used form of promotion—known as detailing—
`consists of visits to physicians by the sales representatives of the
`producers of branded pharmaceuticals. Another instrument for bring-
`ing product information to the attention of prescribing physicians is
`medical-journal advertising. Relative to detailing, journal advertising
`expenditures are modest, although the mix of promotion methods
`varies substantially across products and firms (Berndt et al., 1997).
`
`2(cid:1) At least, this was the situation during the period examined in this paper. The lift-
`ing of the ban on direct-to-consumer advertising and the advent of the World Wide Web
`in the mid-1990s have further expanded the number of relevant information sources.
`
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`Do Pharmaceutical Sales Respond to Scientific Evidence?
`
`555
`
`Despite the intensity of promotion, the overall concern and dis-
`trust for commercial messages is surprising, since the advertising of
`ethical drugs is quite stringently regulated by the FDA.3 Comanor
`(1986) observes that the hypothesis of wasteful or jamming adver-
`tising is insufficiently formalized, and that evidence on its behalf
`is largely impressionistic, relying on comments, letters and editori-
`als of a self-appointed group of physicians and health professionals.
`Indeed, other scholars have claimed that drug advertising performs an
`eminently informative function. Peltzman (1975) proposes that adver-
`tising helps to achieve an efficient rate of diffusion—where the ben-
`efit from increasing the rate just pays the costs required to do so.
`Leffler (1981) shows that product promotion has a significant positive
`effect on the entry success of new drugs yielding important thera-
`peutic gains. However, this evidence must be pitted against results
`demonstrating the role of advertising outlays in building up brand-
`name recall effects that favor established products facing new competi-
`tion by generic entrants (Hurwitz and Caves, 1988). In a similar vein,
`Stern and Trajtenberg (1998) find that physicians who prescribe a nar-
`row set of therapies for a given condition are more likely to prescribe
`highly advertised drugs.
`In one of the most detailed studies of pharmaceutical advertis-
`ing, Berndt et al. (1997) examine the effect of marketing investments
`on the growth and changing composition of the antiulcer-drug mar-
`ket.4 The authors find that the effect of these investments was sub-
`stantial and long-lived, although it partly spilled over to competing
`drugs. They also show that the second entrant’s intense promotion
`efforts were instrumental in overthrowing the market-share leader-
`ship of the incumbent. Finally, they hint—but do not explicitly test
`empirically—that advertising was more effective when it interacted
`with a superior bundle of product-quality attributes, such as lower
`dosage or fewer side effects.
`Market power in prescription-drug markets seems to rest as
`much upon habit persistence as upon fears that serious adverse con-
`sequences (such as a malpractice lawsuit) will follow an inappropriate
`
`3(cid:1) Any material distributed by pharmaceutical companies must carry the “full pack-
`age insert,” i.e., the complete product information reviewed by the agency as part of the
`drug approval process. Also, the advertising of drugs for nonapproved indications is
`prohibited, and comparative advertising must be supported by well-controlled clinical
`studies. Finally, comparison of side-effect profiles is not allowed, because most drug
`studies are not designed to assess the incidence of adverse interactions (Kessler and
`Pines, 1990).
`4(cid:1) I am indebted to Ernie Berndt for providing their data, which is used in this
`paper.
`
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`

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`Journal of Economics & Management Strategy
`
`choice. However, this power may be diminished when objective infor-
`mation about the quality of competing products is available (Scherer,
`1990). In the pharmaceutical industry, government-mandated testing
`requirements coalesce with the incentives of academic clinicians
`to assist in the production of impartial information—through the
`publication of clinical-trial results in medical journals. While many
`studies have sought to inform the debate between the persuasive and
`informative nature of pharmaceutical advertising, none has explicitly
`considered published clinical trials as an alternative information
`conduit. This paper is the first to use both advertising data and
`clinical-research output measures to examine the effect of advertising
`and science on diffusion, as well as the complex relationship between
`promotion efforts and scientific sources of information.
`
`3. The Antiulcer-Drug Market
`3.1 History
`The antiulcer-drug market has been extensively studied by applied
`economists over the past five years, so I will provide only a brief
`review here. The interested reader should refer to Berndt et al. (1997)
`and Suslow (1996) for a more complete exposition. H2 antagonists
`heal ulcers by reducing the secretion of acids in the stomach, and are
`effective in several contexts. Originally approved to cure gastric ulcers
`(located in the stomach), and subsequently for the treatment of peptic
`and duodenal ulcers, their introduction (starting in 1977 with cime-
`tidine) suppressed the need for costly surgeries, allowing treatment
`on an outpatient basis. Later, the FDA approved H2 antagonists as
`preventive treatments, and most importantly, for the treatment of gas-
`troesophageal reflux disease (GERD)—a nonulcerous condition whose
`mild manifestation is more commonly known as heartburn. Finally,
`the liquid formulation of these drugs is also used by hospitals for the
`treatment of patients severely burned or bleeding.
`The antiulcer-drug market can be segmented into three distinct
`submarkets: antacids, H2 antagonists, and proton-pump inhibitors.
`Antacids (Mylanta, Maalox) were the first drugs introduced on the
`market, and are still considered good pain relievers (they relieve
`symptoms within minutes). They do not, however, inhibit acid secre-
`tion, and are therefore poor substitutes for the therapeutic class
`considered here.
`Beginning in 1989, a new generation of drugs, known as proton-
`pump inhibitors, appeared on the market. Proton-pump inhibitors
`operate by completely shutting down acid secretion, and seem to pro-
`vide an improvement on the performance of H2 antagonists. Today,
`
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`

`Do Pharmaceutical Sales Respond to Scientific Evidence?
`
`557
`
`TABLE I.
`H2-Antagonist Drugs
`FDA Indications
`
`Drug
`
`Molecule
`
`Firm
`
`Entry Duodenal Duod. Ulcer Gastric
`Date
`Ulcer Maintenance Ulcer GERD
`
`Tagamet Cimetidine SmithKline Aug. 77 Aug. 77
`Zantac
`Ranitidine Glaxo
`June 83
`June 83
`Pepcid
`Famotidine Merck
`Nov. 86 Aug. 86
`Axid
`Nizatidine Eli Lilly
`May 88 May 88
`
`Apr. 80
`May 86
`Oct. 86
`Apr. 88
`
`Dec. 82 Mar. 91
`June 85 May 86
`Oct. 88 Dec. 91
`July 91
`
`H2 antagonists are available over the counter, and Prilosec has become
`the main prescription drug for the treatment of infections of the gas-
`trointestinal tract.5
`
`3.2 Overview of the Data
`Given the competitive history described above, the empirical exercise
`will be limited to the period beginning in August 1977 (date of entry
`on the US market for the pioneer drug), and ending in May 1993
`(before Prilosec’s rise to market dominance and Tagamet’s imminent
`patent expiration). The first H2 antagonist, Tagamet, was launched
`by SmithKline in 1977, and soon became one of the most popular
`prescription drug ever sold. Since then, three alternative H2-blocker
`medications have entered the market: Zantac (Glaxo) in 1983, Pepcid
`(Merck) in 1986, and Axid (Eli Lilly) in 1988. A brief synthesis of the
`main product characteristics for these four drugs appears in Table I.
`The date for a specific indication corresponds to the time of FDA
`approval.
`The dataset draws upon two distinct sources of information.
`First, product-level data on monthly sales, prices, advertising lev-
`els, and other product characteristics originates with the market
`research firm IMS (Philadelphia, Pennsylvania) and is discussed at
`greater length in Section 3.4.6 Second, product-level indices of clinical
`
`5(cid:1) Three other forms of treatment deserve brief mention. Carafate was introduced in
`1981, after Tagamet but before all the other H2 antagonists. Because the required dosage
`is four times a day, it has remained a marginal player. Reglan entered the market in
`1984, but is only approved for GERD. Finally, Cytotec entered in 1988, but is only
`indicated for the prevention of ulcers induced by nonsteroidal antiinflammatory drugs
`(NSAIDs).
`6(cid:1) This data is also described extensively in Azoulay (2001) and in Berndt et al.
`(1997), both in Section 7.3 (pp. 282–295) and in the appendix (pp. 314–321). Although
`both drugstore and hospital markets are covered by IMS audits, the analysis below will
`
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`Journal of Economics & Management Strategy
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`TABLE II.
`Description of Variables in Sample
`
`Variable
`
`Qit
`shareit
`interactionsit
`indicationsit
`dosageit
`pit
`flowdetailingit
`flowdetailing−it
`
`flowdetailingcoit
`
`stkdetailingit
`
`flowjournalit
`
`flowjournal−it
`
`flowjournalcoit
`
`stkjournalit
`
`science1it
`
`science2it
`
`science−it
`
`Definition
`
`Number of monthly patient-days for drug i, in millions
`Market share of drug i (total market includes outside good)
`Number of adverse drug interactions for drug i
`Number of approved FDA indications for drug i
`Daily frequency of administration for drug i
`Real price per daily dose of duodenal ulcer therapy for drug i
`Flow of monthly detailing minutes for drug i, in millions
`Flow of monthly detailing minutes for competitors of drug i,
`in millions
`Flow of monthly detailing minutes for firm selling drug i,
`in millions
`Stock of monthly detailing minutes for drug i,
`in millions (δ = 5%)
`Flow of monthly journal advertising expenditures for drug i,
`in millions of real dollars
`Flow of monthly journal advertising expenditures for
`competitors of drug i, in millions
`Flow of monthly journal advertising expenditures for firm
`selling drug i, in millions
`Stock of monthly journal advertising expenditures for drug i,
`in millions (δ = 5%)
`Stock of market-expanding citations for drug i,
`in hundreds (δ = 0%)
`Stock of comparative citations for drug i,
`in hundreds (δ = 0%)
`Total stock of citations for competitors of drug i,
`in hundreds (δ = 0%)
`
`research output are constructed in Section 3.3 below, using abstracts of
`clinical studies published in the medical literature. The complete list
`of variables and their definitions can be found in Table II. Descriptive
`statistics are displayed in Table III.
`
`3.3 Measuring Clinical-Research Output
`In order to study the response of sales to scientific information,
`as well as the sensitivity of promotion efforts to published clinical
`results, it is necessary to construct measures of relevant scientific
`activity. In the Data Appendix, I describe in detail the construction
`of indices measuring the flows and stocks of scientific information in
`
`rely exclusively on the drugstore-market data, since it accounts for 90% of total dollar
`sales, and hospital use is very different from outpatient use, both in purpose and in
`presentation.
`
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`

`Do Pharmaceutical Sales Respond to Scientific Evidence?
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`559
`
`Variable
`
`Qit
`shareit
`interactionsit
`indicationsit
`dosageit
`pit
`flowdetailingit
`flowdetailing−it
`flowdetailingcoit
`stkdetailingit
`flowjournalit
`flowjournal−it
`flowjournalcoit
`stkjournalit
`science1it
`science2it
`science−it
`
`science1it
`science2it
`stkdetailingit
`stkjournalit
`
`Variable
`
`Qit
`shareit
`interactionsit
`indicationsit
`dosageit
`pit
`flowdetailingit
`flowdetailing−it
`flowdetailingcoit
`stkdetailingit
`flowjournalit
`flowjournal−it
`flowjournalcoit
`stkjournalit
`science1it
`science2it
`science−it
`
`science1it
`science2it
`stkdetailingit
`stkjournalit
`
`TABLE III.
`Descriptive Statistics
`
`(A) For Tagamet (T = 188)
`Mean
`Std. Dev.
`
`27(cid:1)685
`0(cid:1)142
`7(cid:1)096
`2(cid:1)745
`2(cid:1)516
`1(cid:1)057
`0(cid:1)094
`0(cid:1)155
`0(cid:1)254
`1(cid:1)727
`0(cid:1)253
`0(cid:1)337
`0(cid:1)623
`4(cid:1)749
`24(cid:1)929
`−2(cid:1)071
`7(cid:1)301
`
`8(cid:1)904
`0(cid:1)047
`3(cid:1)617
`1(cid:1)089
`1(cid:1)416
`0(cid:1)328
`0(cid:1)036
`0(cid:1)149
`0(cid:1)095
`0(cid:1)656
`0(cid:1)161
`0(cid:1)335
`0(cid:1)248
`1(cid:1)149
`4(cid:1)291
`1(cid:1)973
`6(cid:1)477
`
`Correlation Matrix
`science1it
`1(cid:1)000
`−0(cid:1)903
`0(cid:1)947
`0(cid:1)533
`(B) For Zantac (T = 117)
`Mean
`Std. Dev.
`30(cid:1)477
`14(cid:1)241
`0(cid:1)150
`0(cid:1)068
`0(cid:1)145
`0(cid:1)354
`3(cid:1)248
`1(cid:1)231
`1(cid:1)342
`0(cid:1)476
`1(cid:1)771
`0(cid:1)238
`0(cid:1)133
`0(cid:1)036
`0(cid:1)221
`0(cid:1)098
`0(cid:1)166
`0(cid:1)038
`2(cid:1)233
`0(cid:1)739
`0(cid:1)361
`0(cid:1)195
`0(cid:1)405
`0(cid:1)258
`0(cid:1)661
`0(cid:1)376
`6(cid:1)618
`1(cid:1)256
`6(cid:1)381
`2(cid:1)479
`2(cid:1)962
`1(cid:1)209
`−3(cid:1)150
`3(cid:1)891
`
`Correlation Matrix
`science1it
`1(cid:1)000
`0(cid:1)842
`0(cid:1)911
`0(cid:1)231
`
`Min.
`
`3(cid:1)923
`0(cid:1)021
`0(cid:1)000
`1(cid:1)000
`1(cid:1)000
`0(cid:1)672
`0(cid:1)019
`0(cid:1)000
`0(cid:1)128
`0(cid:1)263
`0(cid:1)010
`0(cid:1)000
`0(cid:1)146
`0(cid:1)701
`12(cid:1)220
`−4(cid:1)720
`0(cid:1)000
`
`Min.
`4(cid:1)190
`0(cid:1)022
`0(cid:1)000
`1(cid:1)000
`1(cid:1)000
`1(cid:1)309
`0(cid:1)048
`0(cid:1)060
`0(cid:1)065
`0(cid:1)378
`0(cid:1)056
`0(cid:1)010
`0(cid:1)057
`1(cid:1)558
`2(cid:1)030
`0(cid:1)040
`−8(cid:1)650
`
`Max.
`
`46(cid:1)424
`0(cid:1)244
`10(cid:1)000
`5(cid:1)000
`4(cid:1)000
`1(cid:1)700
`0(cid:1)199
`0(cid:1)464
`0(cid:1)732
`2(cid:1)576
`1(cid:1)019
`1(cid:1)386
`1(cid:1)317
`7(cid:1)230
`31(cid:1)490
`0(cid:1)000
`21(cid:1)600
`
`science2it
`
`1(cid:1)000
`−0(cid:1)856
`−0(cid:1)227
`
`Max.
`54(cid:1)271
`0(cid:1)276
`1(cid:1)000
`4(cid:1)000
`2(cid:1)000
`2(cid:1)129
`0(cid:1)212
`0(cid:1)456
`0(cid:1)299
`3(cid:1)046
`0(cid:1)940
`1(cid:1)179
`1(cid:1)696
`8(cid:1)621
`13(cid:1)070
`3(cid:1)970
`3(cid:1)300
`
`science2it
`
`1(cid:1)000
`0(cid:1)778
`0(cid:1)406
`
`Continued
`
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`

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`TABLE III.
`continued
`(C) For Pepcid (T = 77)
`Mean
`Std. Dev.
`
`9(cid:1)157
`0(cid:1)045
`0(cid:1)000
`2(cid:1)961
`1(cid:1)000
`1(cid:1)620
`0(cid:1)075
`0(cid:1)348
`0(cid:1)506
`1(cid:1)193
`0(cid:1)173
`0(cid:1)600
`1(cid:1)404
`3(cid:1)360
`1(cid:1)978
`0(cid:1)876
`0(cid:1)283
`
`3(cid:1)739
`0(cid:1)018
`0(cid:1)000
`0(cid:1)715
`0(cid:1)000
`0(cid:1)114
`0(cid:1)023
`0(cid:1)069
`0(cid:1)126
`0(cid:1)406
`0(cid:1)198
`0(cid:1)240
`0(cid:1)735
`1(cid:1)275
`0(cid:1)470
`0(cid:1)459
`5(cid:1)121
`
`Correlation Matrix
`science1it
`1(cid:1)000
`0(cid:1)894
`0(cid:1)828
`−0(cid:1)304
`(D) For Axid (T = 59)
`Std. Dev.
`2(cid:1)337
`0(cid:1)011
`0(cid:1)000
`0(cid:1)492
`0(cid:1)000
`0(cid:1)165
`0(cid:1)024
`0(cid:1)065
`0(cid:1)056
`0(cid:1)546
`0(cid:1)067
`0(cid:1)244
`0(cid:1)067
`0(cid:1)284
`0(cid:1)181
`0(cid:1)240
`4(cid:1)024
`
`Mean
`4(cid:1)935
`0(cid:1)024
`1(cid:1)000
`2(cid:1)390
`1(cid:1)000
`1(cid:1)683
`0(cid:1)114
`0(cid:1)332
`0(cid:1)310
`1(cid:1)611
`0(cid:1)091
`0(cid:1)579
`0(cid:1)176
`1(cid:1)531
`0(cid:1)815
`−0(cid:1)428
`−1(cid:1)811
`
`Correlation Matrix
`science1it
`1(cid:1)000
`−0(cid:1)461
`0(cid:1)581
`−0(cid:1)283
`
`Min.
`
`1(cid:1)729
`0(cid:1)009
`0(cid:1)000
`2(cid:1)000
`1(cid:1)000
`1(cid:1)402
`0(cid:1)031
`0(cid:1)205
`0(cid:1)053
`0(cid:1)226
`0(cid:1)000
`0(cid:1)124
`0(cid:1)253
`1(cid:1)021
`0(cid:1)350
`0(cid:1)000
`−5(cid:1)800
`
`Min.
`0(cid:1)715
`0(cid:1)004
`1(cid:1)000
`2(cid:1)000
`1(cid:1)000
`1(cid:1)484
`0(cid:1)069
`0(cid:1)199
`0(cid:1)200
`0(cid:1)316
`0(cid:1)000
`0(cid:1)117
`0(cid:1)071
`0(cid:1)583
`0(cid:1)720
`−0(cid:1)640
`−6(cid:1)410
`
`Max.
`
`16(cid:1)134
`0(cid:1)076
`0(cid:1)000
`4(cid:1)000
`1(cid:1)000
`1(cid:1)851
`0(cid:1)131
`0(cid:1)505
`0(cid:1)797
`1(cid:1)641
`0(cid:1)906
`1(cid:1)310
`3(cid:1)023
`5(cid:1)532
`2(cid:1)590
`1(cid:1)390
`9(cid:1)710
`
`science2it
`
`1(cid:1)000
`0(cid:1)784
`−0(cid:1)487
`
`Max.
`9(cid:1)207
`0(cid:1)047
`1(cid:1)000
`3(cid:1)000
`1(cid:1)000
`1(cid:1)943
`0(cid:1)217
`0(cid:1)441
`0(cid:1)476
`2(cid:1)268
`0(cid:1)308
`1(cid:1)099
`0(cid:1)372
`2(cid:1)036
`1(cid:1)220
`0(cid:1)000
`6(cid:1)760
`
`science2it
`
`1(cid:1)000
`−0(cid:1)941
`−0(cid:1)302
`
`Variable
`
`Qit
`shareit
`interactionsit
`indicationsit
`dosageit
`pit
`flowdetailingit
`flowdetailing−it
`flowdetailingcoit
`stkdetailingit
`flowjournalit
`flowjournal−it
`flowjournalcoit
`stkjournalit
`science1it
`science2it
`science−it
`
`science1it
`science2it
`stkdetailingit
`stkjournalit
`
`Variable
`
`Qit
`shareit
`interactionsit
`indicationsit
`dosageit
`pit
`flowdetailingit
`flowdetailing−it
`flowdetailingcoit
`stkdetailingit
`flowjournalit
`flowjournal−it
`flowjournalcoit
`stkjournalit
`science1it
`science2it
`science−it
`
`science1it
`science2it
`stkdetailingit
`stkjournalit
`
`

`

`Do Pharmaceutical Sales Respond to Scientific Evidence?
`
`561
`
`pharmaceutical markets, an original methodological contribution of
`this paper. I briefly summarize the process below.
`I select 483 randomized controlled trials (RCTs) pertaining to the
`four H2-antagonist drugs published in 16 prominent general medicine
`and gastroenterology academic journals (these journals are listed in
`the Data Appendix). I examine the control group used in the trial. If
`a placebo or any active substance other than the four H2 blockers is
`used, I assign to the article the label market-expanding science. In the
`case of a comparative drug study between two or more of the H2
`antagonists, the label comparative science is assigned. Conditional on
`the label, I score each RCT using a three-step Likert scale (+1(cid:8) 0(cid:8)−1)
`to assess the negative, neutral, or positive impact of the article: +1
`(respectively −1) is assigned if the treatment effect is significant and
`favors (respectively does not favor) the drug studied. A score of 0 is
`assigned if the treatment effect fails to reach statistical significance. In
`order to capture variation in quality across clinical studies, I weight
`the treatment effect score by the cumulative number of forward cita-
`tions to the original article as of May 2001.
`3.3.1 Flows and Stocks of Scientific Information. The
`final step is to define the variables characterizing the monthly flows
`and stocks of scientific information in the H2-blockers therapeutic
`class. For each market-expanding (respectively comparative) study
`(respectively flow2it) asflow1 it = (cid:1)
`s, pertaining to drug i, published during month t, I define flow1it
`s ws· tesit, where tesit is the score
`received by drug i in trial s published during month t, and ws is the
`weight assigned to study s. The variable flowit = flow1it + flow2it
`lumps together market-expanding and comparative science for each
`drug.
`However, one would not expect the monthly flow of scientific
`information to have an effect on sales. Figure 1 graphs flowit for
`Tagamet and Zantac. Except for the spikes, which correspond to large-
`scale trials published in prestigious journals, it is difficult to discern
`a trend by studying month-to-month variations. Since RCTs provide
`information about the existence and/or usefulness of a molecule, one
`would expect their effect to be long-lived, decaying slowly until better
`evidence appears in the literature. Hence, the effect of scientific infor-
`mation on sales is likely to be better proxied by a stock rather than
`a flow variable. Since clinical results start to accumulate before entry
`on the product market and do not diffuse instantaneously, the origin
`on the time axis was set at m0 − 36 (where m0 denotes the month of
`entry).
`Finally, I allow for the possibility that the stock of clinical output
`decays over time with monthly depreciation rate δ (to be estimated
`
`

`

`562
`
`800
`
`600
`
`400
`
`200
`
`0
`
`-200
`
`-400
`
`-600
`
`-800
`Jan
`
`-75
`
`Journal of Economics & Management Strategy
`
`Tagamet/Cimetidine
`
`Zantac/Ranitidine
`
`Jul-76
`
`Jan
`
`-78
`
`Jul-79
`
`Jan-81
`
`Jul-82
`
`Jan-84
`
`Jul-85
`
`Jan-87
`
`Jul-88
`
`Jan-90
`
`Jul-91
`
`Jan-93
`
`FIGURE 1. CIMETIDINE AND RANITIDINE FLOW OF SCIENTIFIC
`INFORMATION, 1975–1993
`
`scienceit = (1 − δ)sciencei(cid:8) t−1 + flowit =
`
`below). A perpetual inventory model is used to define stock variables
`corresponding to each flow variable defined above:
`t(cid:2)
`(1 − δ)t−τ flowiτ(cid:1)
`τ=m0−36
`3.3.2 Quality of the science Variables. Several issues
`can be raised regarding the method used to compute the science
`indices. First, one could have designed many alternative scoring and
`weighting schemes. One such alternative would have been to choose
`a measure of efficacy (such as the treatment effect itself) as the grade.
`For example, Cockburn and Anis (2001) use clinical studies to build
`regressors in the construction of hedonic price indices for arthritis
`drugs. They collect measures of efficacy, toxicity, and side effects
`for each study and use them to build variables measuring quality
`changes. However, it is difficult to replicate this approach in the con-
`text of the antiulcer-drug market because no homogeneous efficacy
`measure is available for the three conditions studied. For instance,
`ulcer treatment studies refer to healing rates, ulcer maintenance
`studies refer to relapse rates, and GERD studies most often record
`the percentage of patients for whom the symptoms disappeared.
`Measurement devices (endoscopes, pH meters) vary across articles.
`Though the Likert-scale approach is a simplification, it circumvents
`these issues.
`
`

`

`Do Pharmaceutical Sales Respond to Scientific Evidence?
`
`563
`
`Second, are the articles selected authentically scientific? This is
`a concern in light of the evidence that industry is funding clinical tri-
`als, and that frontiers between advertising and research have become
`blurred. Bero and Rennie (1996) document the growing prominence
`of industry sponsorship of clinical studies concurrent with the relative
`shrinking of government support for research.7 This trend is alleged
`to result in a growing distrust of academic medicine by practitioners.
`To examine the relevance of this claim in the data, I reviewed fund-
`ing sources for a subsample of 21 studies included in this analysis.
`Eight articles did not report their funding. The remainder of the stud-
`ies either were totally industry-funded (14%), were partially industry-
`funded (14%), or benefited from some form of industry support (38%),
`for example, through the supply of drugs, placebo tablets, advice on
`experimental design, or help with the statistical analysis. It is difficult
`to ascertain the precise effect of industry funding on the quality of
`clinical trials. The imposition of rigid selection criteria for the journals
`and articles alleviates the concern that science only represents dif-
`ferent measures of advertising: all included studies report the results
`of randomized, controlled trials, and are published by peer-reviewed
`journals with good standing in their field.
`Third, is the weighting scheme adopted justifiable? Adams and
`Griliches note that the flow of ideas is in general difficult to quan-
`tify. Nevertheless, they assert that “the best that can be done at the
`moment is to count papers and