Measuring the Informative and Persuasive Roles of
`
`Detailing on Prescribing Decisions*
`
`Andrew Chingt
`Masakazu Ishihara
`Rotman School of Management
`University of Toronto
`
`First draft: May 5, 2008
`This draft: April 27, 2010
`
`*We thank Abe Dunn, A vi Goldfarb, Nitin Mehta, and seminar participants at the University of Toronto,
`
`Conference on Health Economics and the Pharmaceutical Industry in Toulouse, 2008 IIOC Conference, 2008
`
`North American Econometric Society Summer Meeting, 2009 AEA Annual Meeting, 2009 CEA Annual Confer(cid:173)
`
`ence, and 2009 Marketing Science Conference for their helpful comments. All remaining errors are ours. We are
`
`grateful to CurrentPartnering for sharing their report on co-marketing agreement with us. We also acknowledge
`
`the financial support provided by the Michael Lee-Chin Family Institute for Corporate Citizenship at Rotman
`
`School of Management.
`
`tPlease direct all correspondence to: Andrew Ching, Rotman School of Management, University of Toronto,
`
`105 St. George Street, Toronto, ON, CANADA M5S 3E6. Phone: 416-946-0728. Fax: 416-978-5433. Email:
`
`andrew.ching@rotman.utoronto.ca.
`
`PLAINTIFFS'
`TRIAL EXHIBIT
`PTX0433
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`MEDA_APTX03502998
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`CIP2097
`Argentum Pharmaceuticals LLC v. Cipla Ltd.
`IPR2017-00807
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`Measuring the Informative and Persuasive Roles of Detailing on
`
`Prescribing Decisions
`
`Abstract
`
`In the pharmaceutical industry, measuring the importance of informative and persuasive
`
`roles of detailing is crucial for both drug manufacturers and policy makers. However, little
`
`progress has been made in disentangling the informative and persuasive roles of detailing in
`
`empirical research. In this paper, we provide a new identification strategy to separately identify
`
`these two roles. Our key identification assumption is that the informative component of detailing
`
`is chemical specific while the persuasive component is brand specific. Our strategy is to focus
`
`on markets where some drug manufacturers engage in a co-marketing agreement. Under a
`
`co-marketing agreement, two companies market the same chemical using two different brand(cid:173)
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`names. With our identification assumption, the variations in the relative market share of these
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`two brands, together with their brand specific detailing efforts, would allow us to measure the
`
`persuasive component of detailing. The variations in the market share of chemicals, and the
`
`detailing efforts summed across brands made of the same chemical, would allow us to measure
`
`the informative component of detailing. Using the data from the Canadian market for ACE(cid:173)
`
`inhibitor with diuretic, we find evidence that our identification strategy could outperform the
`
`traditional way of identifying these two effects. We find that both the informative and persuasive
`
`components are strong in this market. We also find that patients could be worse off if the
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`government bans detailing for ACE-inhibitor with diuretic.
`
`Keywords: Detailing, Informative Role, Persuasive Role, Prescription Drugs, Decisions Under
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`Uncertainty, Diffusion
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`Introduction
`
`In the pharmaceutical industry, measuring the importance of informative and persuasive roles of
`
`detailing is crucial for both drug manufacturers and policy makers. Understanding the relative
`
`importance of these two roles could help drug manufacturers allocate resources to detailing more
`
`efficiently. If the persuasive role is important, firms could create artificial product differentiation
`
`by increasing their detailing efforts. On the contrary, if detailing is mainly informative and its
`
`persuasive role is weak, the effectiveness of detailing intensity will highly depend on the actual
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`quality of drugs (i.e., side-effects and efficacy profiles). Among policy debates, many people
`
`believe that detailing is mainly persuasive and consumers will be better off if the industry
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`reduces their detailing budget. Consequently, there are frequent calls for the industry to restrict
`
`detailing activities. However, if detailing is mainly informative in nature, putting restrictions
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`on it might slow down the adoption rate of new innovative drugs. Consequently, this would
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`not only hurt manufacturers' profits and their incentives to innovate, but also lower consumer
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`welfare.
`
`Despite its importance, little progress has been made in disentangling the informative and
`
`persuasive roles of detailing. The main difficulty is that both effects would likely have positive
`
`impacts on the demand for prescription drugs. If one only observes sales and detailing efforts
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`over time, it is hard to disentangle these two roles. In this paper, we provide a new identification
`
`strategy to separately identify the persuasive and informative roles of detailing. Our key identi(cid:173)
`
`fication assumption is that the informative component of detailing is chemical specific while the
`
`persuasive component is brand specific. Our strategy is to focus on a market where some drug
`
`manufacturers engage in a co-marketing agreement. Under such an agreement, two companies
`
`market the same chemical under two different brand-names. With our identification assump(cid:173)
`
`tion, the variation in the relative market share of these two brands, together with their brand
`
`specific detailing efforts, would allow us to measure the persuasive component of detailing. After
`
`controlling for the persuasive effect, the variation in the market share of chemicals, and the cor(cid:173)
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`responding chemical specific detailing efforts summed across brands made of the same chemical,
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`would allow us to measure the informative component of detailing. For instance, if detailing
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`does not play any persuasive role at all, our assumptions would imply that the market shares
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`for two brand-name drugs made of the same chemical should be roughly the same over time
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`even if the det ailing efforts are different across these two brands (assuming other marketing-mix
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`variables are about the same across brands).
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`More specifically, t o model persuasive det ailing, we follow the previous literature (e.g.,
`
`Nerlove and Arrow 1962) and allow a brand specific persuasive det ailing goodwill st ock t o enter
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`physicians' utility functions. To model informative det ailing, we consider two alternative models
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`of informative det ailing that have been used in the literature. The first model follows Ching and
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`Ishihara (2010), which models informative det ailing as a means t o build/ maintain the measure
`
`of physicians who know the most updated information about drugs. The second model follows
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`Narayanan et al. (2005), in which detailing conveys noisy signals about the true quality of drugs
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`to physicians.
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`Our identification strategy applies to both product level data and individual level data.
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`As an application, we apply it to the product level data from the market of ACE-inhibitor
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`with diuretic in Canada. 1 This market has three brand-name drugs: Vaseretic, Zestoretic, and
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`Prinzide. Zestoretic and Prinzide are made of the same chemicals, but are co-marketed by two
`
`different companies. To demonstrate the usefulness of our identification strat egy, in addition
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`t o estimating the full model with all three brands, we also estimat e two versions with only
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`two brands: Zestoretic and Prinzide, assuming that in one version, we treat the two brands as
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`the same 1 chemical (i.e., we use our co-marketing identification argument), and in the other
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`version, we treat the two brands as different two chemicals (i.e., we do not use the co-marketing
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`identification argument). We argue that the identification of the informative and persuasive
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`effects in the 2-chemical version relies more heavily on the functional form assumption.
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`In
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`particular, we find that the estimation results are counterintuitive in the 2-chemical version -
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`the persuasive effect of detailing is negative and insignificant. On the contrary, the estimation
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`results from the 1-chemical version are much more sensible - the persuasive effect is positive
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`and significant, regardless of the way we model the informative det ailing.
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`1 Although we use product level data to illustrate our identification strategy, it should be emphasized that
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`the argument applies to individual level data as well. The basic identification ideas are the same except that we
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`will need to set up individual level likelihood when estimating the parameters.
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`Based on the parameter estimates from the full model with three brands, we investigate
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`the importance of informative and persuasive detailing by simulating our model in the case
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`of ACE-inhibitor with diuretic. We find that both informative and persuasive components
`
`are important. In particular, the informative component is mainly responsible for the growth
`
`of the demand for chemicals, and the persuasive component mainly influences brand choice.
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`Furthermore, to examine the overall impact of detailing on patient welfare, we use compensating
`
`variation to measure changes in the patient's welfare over time from banning detailing activities.
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`Our simulation results suggest that banning detailing could cost a patient as large as $160 per
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`prescription during our sample period in the Canadian ACE-inhibitor with diuretic market.
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`The rest of the paper is organized as follows. Section 2 reviews the literature and discusses
`
`the background of the co-marketing agreement. Section 3 describes the demand models. Section
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`4 describes the data. Section 5 discusses the results. Section 6 is the conclusion.
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`2 Literature Review and Co-marketing Agreement
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`2.1 Previous Literature on Persuasive Detailing
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`How does detailing affect physicians' prescribing decisions? Leffler (1981) argues that detailing
`
`plays both informative and persuasive roles. He finds that newly introduced drugs tend to
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`receive more detailing than older drugs, and interprets this as evidence that supports informative
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`detailing. He argues that physicians are relatively unfamiliar with new drugs and hence if
`
`detailing provides information about drug's benefits and side-effects, drug manufacturers would
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`spend more detailing efforts for newer drugs. However, he also finds that drug companies
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`still spend significant amount of detailing efforts on old drugs and target older physicians. He
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`interprets this as evidence for its persuasive role, assuming that older physicians have already
`
`known the older drugs' efficacy and side-effect profiles.
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`Hurwitz and Caves (1988) find that pre-patent expiration cumulative detailing efforts slow
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`down the decline in post-patent expiry market shares of brand-name drugs. They interpret
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`this as evidence for its persuasive role. Rizzo (1999) also finds evidence that detailing lowers
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`the price elasticity of demand and argues that it supports persuasive detailing. However, it
`
`should be pointed out that the results from Hurwitz and Caves (1988) and Rizzo (1999) are
`
`also consistent with informative detailing. As argued by Leffler (1981), informative detailing
`
`reduces the uncertainty about drug qualities, and hence could also achieve similar empirical
`
`implications.
`
`Narayanan et al. (2005) is the first paper that structurally estimates the informative and
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`persuasive roles of detailing in the pharmaceutical market. They extend the framework of Erdem
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`and Keane (1996). Their identification argument builds on Leffler (1981) - they assume that
`
`drug companies know the true quality of their products when launching them, and informative
`
`detailing provides physicians with noisy signals about their products' true qualities. With this
`
`assumption, physicians will eventually learn the true quality of the drugs and detailing no longer
`
`plays any informative role in the long-run. As a result, the long-run correlation between sales
`
`and cumulative detailing efforts will identify the parameters that capture the persuasive role of
`
`detailing. The product diffusion paths then identify the parameters that capture the informative
`
`role. It should be emphasized that in their framework, in order to separately identify the
`
`informative and persuasive roles of detailing, it is crucial that: (i) one assumes detailing does
`
`not play any informative role in the long-run; (ii) the data set needs to be long enough so that
`
`it captures part of the product lifecycle after learning is complete. 2 In contrast, this modeling
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`assumption and data requirement are not necessary for our identification strategy.
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`Another related paper is by Ackerberg (2001). He argues that one can empirically dis(cid:173)
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`tinguish informative and persuasive effects of advertising by examining consumers' purchase
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`behavior conditional on whether they have tried the product before. His insight is that ad(cid:173)
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`vertisements that give consumers product information should primarily affect consumers who
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`have never tried the brand, whereas persuasive advertisements should affect both inexperienced
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`and experienced consumers. His identification argument requires one to observe individual level
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`2 Anand and Shachar (2005), Byzalov and Shachar (2004), Chan et al. (2007), Mehta et al. (2008), Narayanan
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`and Manchanda (2009) rely on similar identification arguments to estimate the informative and persuasive roles
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`of advertising using individual level data.
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`panel data, while our identification strategy applies as long as one observes product level panel
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`dat a.
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`2.2 Co-marketing Agreement
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`Co-marketing in the pharmaceutical industry is a marketing practice where a company in ad(cid:173)
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`dition t o its own, uses another company's sales force t o promot e the same product , and allow
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`anot her company to use a different brand name.3 According to CurrentPartnering (2009), the
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`t ot al number of co-marketing deals announced in the United States between 2000 and 2008 is
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`208, and the yearly number has remained at fairly steady levels. One reason why a company
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`that develops the drug is willing to partner with another company could be because it requires
`
`high fixed costs to build a sales force. The sales force in the pharmaceutical industry requires
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`extensive training because they are required to know the clinical trials results of not only the
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`drug being promoted, but also their rivals' drugs. Instead of paying such a high fixed cost, a
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`company which is short in their sales force of promoting a certain category of drugs (say a high
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`blood pressure drug) might find it worthwhile to sign a co-marketing agreement with another
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`company, and charge its part ner a royalty fee.
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`This type of marketing agreement has also appeared in the automobile industry (Sullivan
`
`1998; Lado et al. 2003). Furthermore, for industrial products, it is common that different firms
`
`market essentially identical products using their own brand-names (Saunders and Watt 1979;
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`Bernitz 1981). In some countries, firms also market generic drugs with a brand name (Birkett
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`2003). Under these environments, we expect that our identification arguments could also be
`
`applied.
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`3There is another type of related arrangement which is called co-promotion agreement where two or more
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`firms market the same drug under one brand-name.
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`3 Model
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`We now turn to describe the models that will be used to implement our new identification
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`strategy for informative and persuasive detailing. We consider two structural models that have
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`been developed in the literature. They differ in terms of how to model the role of informative
`
`detailing. The first model (Model CI) extends Ching and Ishihara (2010) . They model infor(cid:173)
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`mative detailing as a means to build/maintain the measure of physicians who know the most
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`updated information about drugs. The second model (Model NMC) follows Narayanan et al.
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`(2005), who model detailing as a way of conveying noisy signals about the true quality of drugs
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`to physicians. In both models, we model the persuasive role of detailing by including a detailing
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`goodwill stock in the utility function for physicians. These two models allow us to capture the
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`role of informative detailing under different environments. For example, when manufacturers
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`know the true quality of their drugs from the beginning of the product lifecycle, Model NMC
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`is particularly relevant. When manufacturers do not know the true quality and use detailing
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`to inform or remind physicians of the most updated information, Model CI is more appropri(cid:173)
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`ate. Since these two models generate different empirical implications, it is of our interest to
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`investigate how our identification strategy performs regardless of the way we model informative
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`detailing.
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`The following basic setup is common in both models. We consider a set of brand-name
`drugs, which treat the same illness using similar chemical mechanisms. Let j = 1, ... , J indexes
`brands, j = 0 denotes an outside alternative, which represents other close substitutes. Some
`
`of the brands may be marketed under a co-marketing agreement and are made of the same
`chemical. Let k = 1, ... , K indexes for chemicals, where K :=::; J. Let Ak be the set of brands
`
`that are made of chemical k. We assume that each brand is made of one of K chemicals. The
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`characteristics of brand j E Ak are given by Pj and qk, where Pj is the price of product j, and qk
`
`is the mean quality level of chemical k. Physicians are imperfectly informed about the chemical's
`mean quality level, qk. Let I(t) = (I1(t), ... ,IK(t)) be a vector of public information sets that
`describe the most updated belief about q = ( q1, ... , qK) at time t.
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`As we mentioned, CI and NMC differ in terms of how they model informative detailing.
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`Model CI assumes that J(t) is updated by a representative opinion leader based on past patients'
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`experiences with the chemical. 4 This is the only role that he/she plays. For each chemical k, a
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`physician either knows Ik(t), or Ik, which is the initial prior that physicians have when a drug
`made of chemical k is first introduced. 5 Let Mkt be the measure of physicians who know h(t).
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`In CI, Mkt depends on the cumulative detailing efforts at time t. The learning process for J(t)
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`is similar in NMC; however, they assume that detailing does not influence Mkt· More precisely,
`they assume Mkt = 1, Vk, t. Moreover, other than consumption experience signals, detailing also
`provides noisy signals about the true quality of the chemicals for updating the I(t).
`
`Our key identification assumptions are: 1) informative detailing is chemical-specific; and
`
`2) persuasive detailing is brand-specific. The first assumption implies: (a) h(t) is updated based
`
`on past patients' experiences for all products made of chemical k; (b) in Model CI, Mkt depends
`
`on the sum of the cumulative detailing efforts for all drugs made of chemical k; and (c) in Model
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`NMC, in addition to past patients' drug experiences, h(t) are also updated based on the sum
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`of the detailing signals for all drugs made of chemical k. The second assumption implies that
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`the persuasive detailing goodwill stock for brand j is built based only on the detailing efforts
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`for brand j. In what follows, we will describe Model CI first, and then Model NMC.
`
`3.1 Model CI (Ching and Ishihara 2010)
`
`3.1.1 Updating of the Information Set
`
`A drug is an experienced good. Consumption of a drug provides information about its quality.
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`It is assumed that physicians and patients in the model can measure drug qualities according to
`
`4 A representative opinion leader captures the following intuition. The medical continuing education litera(cid:173)
`
`ture finds that opinion leaders are an important source of information for general physicians (e.g., Haug 1997,
`
`Thompson 1997). In Medicine, opinion leaders are physicians who specialize in doing research in a particular
`
`field (e.g., cardiovascular). The research focus of their career requires them to be much more updated about the
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`current evidence about the drugs used in the field.
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`5For simplicity, we assume that physicians and the representative opinion leader share the same initial prior
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`belief. In general, we can allow them to be different.
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`a fixed scale. For example, a patient can measure quality in terms of how long he/she needs to
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`wait before the drug becomes effective to relieve his/her symptoms, how long his/her symptoms
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`would be suppressed after taking the drug, or how long the side-effects would last.6
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`Each patient i's experience with the quality of a drug made of chemical k at timet (fjikt)
`
`may differ from its mean quality level qk. As argued in Ching (2000; 2010; 2011), the difference
`
`between fiikt and qk could be due to the idiosyncratic differences of human bodies in reacting to
`
`drugs. An experience signal may be expressed as,
`
`(1)
`
`where 8ikt is the signal noise. We assume that 8ikt is an i.i.d. normally distributed random
`
`variable with zero mean, and the representative opinion leader's initial prior on qk (lk) is also
`
`normally distributed:
`
`(2)
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`The representative opinion leader updates the public information set at the end of each period
`
`using the experience signals that are revealed to the public. The updating is done in a Bayesian
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`fashion . In each period, we assume that the number of experience signals revealed is a random
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`subsample of the entire set of experience signals. This captures the idea that not every patient
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`revisits and discusses his/her experiences with physicians, and not every physician shares his/her
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`patients' experiences with others.
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`According to the Bayesian rule (DeGroot 1970), the expected quality is updated as follows:
`
`(3)
`
`where iikt is the sample mean of all the experience signals that are revealed in period t; ik(t) is a
`
`Kalman gain coefficient, which assigns the updating weight to iikt· Note that both ik(t) and the
`perception variance, O"~(t + 1), are functions of the variance of the signal noise (O"~), perceived
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`6 0bviously, drug qualities are multi-dimensional. Following Ching (2010), we implicitly assume patients are
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`able to use a scoring rule to map all measurable qualities to a one-dimensional index. It is the value of this
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`one-dimensional index that enters the utility function.
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`variance (O"~(t)), the quantities sold at timet for all drugs made of chemical k (n~), 7 and the
`proportion of experience signals revealed to the public (i'l:). They can be expressed as:
`
`and
`
`(4)
`
`The above expressions imply: ( i) "k ( t) increases with O"~ ( t); ( ii) after observing a sufficiently
`
`large number of experience signals for a product , the representative opinion leader will learn
`
`about qk, at any arbitrarily precise way (i.e., O"k(t) ---* 0 and E[qkii(t)] ---* qk as the number of
`
`signals received grows large).
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`3.1.2 Detailing and Measure of Well-Informed Physicians
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`There is a continuum of physicians with measure one. They are heterogeneous in their informa(cid:173)
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`tion sets. A physician is either well-informed or uninformed about chemical k. A well-informed
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`physician knows the current information set maintained by the representative opinion leader,
`
`i.e., h(t). An uninformed physician only knows the initial prior, i.e., l_k. This implies that the
`
`number of physician types is 2K . 8
`
`The measure of well-informed physicians for chemical k at time t , Mkt , is a function of
`
`Mkt- 1 and Dlt, ... , DJt· For simplicity, we assume that this function only depends on Mkt- 1
`and D~ = EjEAk Djt, i.e., Mkt = f(Mkt-1, D~). We assume that f(Mkt-1, .) is monotonically
`increasing in Df. To capture the idea that physicians may forget, we assume that f(M, 0) ::::;
`
`M,'VM.
`
`Following Ching and Ishihara (2010), in our econometric model, we capture the relationship
`
`between Mkt and (Mkt-1, Df) by introducing a detailing goodwill stock, Gfn which accumulates
`
`as follows:
`
`7 nf is the total quantity prescribed for chemical k at time t, including free samples measured in number of
`prescriptions.
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`8For justifications of this modeling assumption, see Ching and Ishihara (2010).
`
`(5)
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`where cjy1 E [0, 1] is the depreciation rate. We specify the relationship between Mkt and Gkt as:
`exp(f3o + /31 G{t)
`(
`1 + exp f3o + f31Gkt
`
`(6)
`
`M _
`kt -
`
`I ) ·
`
`3.1.3 Prescribing Decisions
`
`Now we turn to discuss how physicians make their prescribing decisions. Each physician t akes
`
`the current expect ed utility of his/her patients into account when making prescribing decisions.
`
`Physician h's objective is to choose dhij (t) t o maximize the current period expected utility for
`
`his/her patients:
`
`E[ L Uijt . dhij ( t) IIh ( t)],
`
`jE{O,l, ... ,J}
`
`(7)
`
`where dhij(t) = 1 indicates that alternative j is chosen by physician h for patient i at time t,
`and dhij(t) = 0 indicates otherwise. We assume that Ej dhij(t) = 1. The demand system is
`
`obtained by aggregating this discrete choice model of an individual physician's behavior.
`
`We assume that a patient's utility of consuming a drug can be adequately approximated
`
`by a quasilinear utility specification, additively separable in a concave subutility function of
`
`drug return, and a linear t erm in price. The utility of patient i who consumes drug j made of
`chemical k at time t is given by the following expression:
`
`(8)
`
`where ai is a brand-specific intercept; r is the risk aversion parameter; 1rp is the utility weight
`for price; ( ~ilt + (ikt + eijt) represents the distribution of patient heterogeneity; and k, l indexes
`nests. 9 ~ilt, (ikt, and eijt are unobserved to the econometrician but observed to the physicians
`
`when they make their prescribing decisions. We assume that ~ilt, (ikt and eijt are i.i.d. extreme
`
`value distributed. In this specification, r represents the coefficient of absolute risk aversion.
`
`Also, we allow a brand-specific intercept to capture time-invariant differences among drugs.
`
`9This is equivalent to modeling physicians' choice as a three-stage nested process, where they choose between
`
`the inside goods and the outside good in the first stage, choose one of the chemicals in the second stage, and an
`
`alternative made of the chemical chosen in the second stage.
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`Note that iiikt is observed neither by physicians nor patients when prescribing decisions
`
`are made. It is observed by physicians/patients only after patients have consumed the drug,
`
`but it remains unobserved by the econometrician. P hysicians make their decisions based on the
`
`expected utility of their patients. Let I (t) and Jh(t) denot e the representative opinion leader's
`
`information set and physician h's information set at time t , respectively. For drug j E A k, if
`physician his well-informed about chemical k at time t, his/her expected utility will be:
`
`E[uift llh(t) ]
`
`E[uijt llk(t) ] + "fpGft + "fs F Sjt
`= aj - exp( - r E[qk ii(t )] + ~r2 (0'~(t) + 0'~))- 1fpPjt
`+ "fpGft + "!s F Sit + c:;itt + (ikt + eift,
`
`(9)
`
`where G_ft is a detailing goodwill stock for drug j at time t, and 'YP captures the effect of
`
`persuasive detailing; F Sit is the amount of free samples given for drug j at time t, and 'Ys
`
`captures the effect of free samples. Similar to Git, we assume that G_ft accumulates as follows:
`
`(10)
`
`We emphasize that G_ft is drug j specific rather than chemical k specific. Furthermore, we allow
`
`the depreciation rat es t o be different for G{t and G_ft. We should also note that Ching and
`
`Ishihara (2010) just focus on modeling the informative role of det ailing, and they do not allow
`
`for G_ft in the utility function. They also do not control for free samples.
`
`If physician his uninformed about chemical kat timet, his/her expected utility of choosing
`
`drug j E Ak becomes:
`
`E[uiftllk] + "fpGft +"(sF Sit
`ai- exp(-rrj_k + ~r2(Q:~ + 0'~)) -1fpPjt
`+"fpGft + "fsFSft + c:;ilt + (ikt + eift·
`
`(11)
`
`It should be noted that patient heterogeneity components of the utility function ( <:;ilt, (ikt, eift)
`
`reappear in the expected utility equation because they are stochastic only from the econometri(cid:173)
`
`cian's point of view.
`
`Equations (8)-(11) apply only to the inside goods. In each period, physicians may also
`
`choose an outside alternative that is not included in our analysis (i.e., other non-bioequivalent
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`drugs). We assume the expected utility associated with the outside alternative takes the follow(cid:173)
`
`ing functional form:
`
`(12)
`
`The time trend of the outside alternative allows the model to explain why the t ot al demand for
`
`inside goods may increase or decrease over time.
`
`The quantity demand for drug j E Ak, nit, can be expressed as,
`
`where Sizet is the size of the market, S(jl·) is the market share of drug j, Ejt represents a
`measurement error, and ed is a set of demand side parameters.
`
`(13)
`
`3.2 Model NMC (Narayanan et al. 2005)
`
`Many elements in Model NMC are similar to Model CI. Therefore, we will only discuss the
`
`elements that are specific t o Model NMC. All the variables introduced in the previous section
`
`will be used here without repeating the descriptions.
`
`3.2.1 Updating of the Information Set
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`In Model NMC, in addition to consumption experience signals, detailing provides physicians
`
`with noisy signals about the true quality of drugs. Let ij~kt be the detailing signal about the
`
`quality of chemical k that physician h receives at time t. Similar to consumption experience
`
`signals, it may be expressed as,
`
`where f)hkt is the signal noise. We assume that f)hkt is an i.i.d. normally distributed random
`
`variable with zero mean:
`
`(14)
`
`fJhkt
`
`rv N(O, O"~).
`
`(15)
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`Signals from patients' experiences and detailing are used to update I(t + 1) in a Bayesian
`
`fashion. According to the Bayesian rule (DeGroot 1970), the expected quality is updated as
`
`follows:
`
`where fltt is the sample mean of all the detailing signals for chemical k in period t. 10 Note that
`
`unlike Model CI, the expected quality is updated based on consumption signals and detailing
`
`signals. ik(t) and wk(t) are expressed as
`
`and
`
`(17)
`
`where K,d is a scaling parameter similar to K,.
`
`ik and wk can be interpreted as the weights that
`
`physicians attach to consumption experiences and detailing efforts in updating its expectation
`
`about the level of qk.
`
`The perception variance at the beginning of timet+ 1 is given by (DeGroot 1970):
`
`(18)
`
`Physicians' prescribing decisions are identical to those of Model CI except that all physi(cid:173)
`
`cians are informed of I(t), i.e., Mkt = 1 Vk, t.
`
`4 Background and Data Description
`
`4.1 Background
`
`Now we turn to discuss the Canadian market of ACE-inhibitor with diuretic in Canada. ACE-
`
`inhibitor works by limiting production of a substance that promotes salt and water retention in
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`the body. Diuretic prompts the body to produce and eliminate more urine. This helps in lowering
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`blood pressure. This class of combination drugs are usually not prescribed until therapy is
`
`already underway. The majority of Canadian have some form of coverage for prescription drugs.
`
`In 1995, it is estimated that 88% of Canadian had coverage: 62% were covered under private
`
`plans, 19% under provincial plans, and 7% were covered under both. 11 Provinces subsidize
`
`the cost of prescription drugs for at least some sectors of the population, most notably seniors
`
`and social assistance recipients. Patented drug prices are regulated in Canada by the Patented
`
`Medicine Prices Review Board (PMPRB). There are two components to this price regulation.
`
`One is the limit on increases of patented drugs already on the market; the other is the limit on
`
`introductory prices of new patented drugs. According to PMPRB guidelines, the prices of most
`
`new drugs may not exceed the maximum price of other drugs that treat the same disease.
`
`4.2 Overview of the Data
`
`Data sources for this study come from IMS Canada, a firm specializes in collecting sales and
`
`detailing data for the Canadian pharmaceutical industry. The revenue data is drawn f