the effects of publicity on demand: the case of anti-cholesterol drugs

The Effects of Publicity on Demand:

The case of anti-cholesterol drugs

Andrew Ching∗

Rotman School of ManagementUniversity of Toronto

Robert ClarkInstitute of Applied Economics

HEC Montreal

Ignatius HorstmannRotman School of Management

University of Toronto

Hyunwoo LimRotman School of Management

University of Toronto

November 16, 2010

∗This is work-in-progress. Comments are welcome. Please direct all correspondence to: Andrew Ching, Rotman Schoolof Management, University of Toronto, 105 St George Street, Toronto, ON, CANADA M5S 3E6. Phone: 416-946-0728.Fax: 416-978-5433. Email: [email protected].

Abstract

This study investigates the effect of publicity on consumer demand. Although the marketing litera-

ture suggests that publicity can have an important influence on consumers’ choices, only a few studies

have attempted to quantify how media coverage can affect consumer demand. The main obstacle to

measuring the impact of publicity is that data on media coverage are difficult to interpret. To over-

come this obstacle, we develop an algorithm to collect information presented in news articles and to

map this information into a multi-dimensional attribute space. By applying our algorithm to news

articles covering statins (a class of anti-cholesterol drugs) accessible in Canada from 1993-2004, we

find evidence that media coverage plays a role in influencing physicians’ choice of statins. Our results

suggest that not all forms of publicity are equal. We find that comparison articles are much more

effective than non-comparison articles. Moreover, we find that the most effective type of publicity is

to provide information on the cholesterol-lowering ability of the drug, and the second most effective

type of publicity is about side-effects. Surprisingly, despite the fact that the ultimate goal of taking

anti-cholesterol medication is to reduce risks of heart disease, publicity related to this dimension has

very little impact on demand. We also find evidence that physicians/patients may generalize clinical

evidence on individual drug specific ability to reduce heart disease risks to the whole class of statins.

The results suggest that manufacturers of the drugs which are more potent in reducing cholesterol could

free-ride on the heart disease clinical evidence for other drugs. Our results could help firms decide which

types of post-marketing clinical trials to invest.

Keywords: Publicity, Consumer Choice, Pharmaceutical Marketing

1 Introduction

Publicity is an important marketing communication tool for firms. It could have a strong impact on pub-

lic awareness at a considerably lower cost than advertising, and function as a substitute/complement for

direct-to-consumer advertising (DTCA).1 Furthermore, consumers may give more credence to publicity

than to advertising, since publicity is generally conveyed to consumers in the form of news. Despite its

potential importance, previous research in marketing seldom investigated how publicity affects demand.

This is mainly because collecting publicity data and interpreting them is very challenging. This paper

contributes to the literature by (i) proposing a new method to interpret publicity data by mapping

the information in each news article (or broadcast) to a multi-dimensional attribute space, and (ii)

investigating how different types of publicity affect consumers’ demand, in particular, in the context of

prescription drugs.

More specifically, we study the impact of publicity on the demand for a class of prescription drugs

called statins, which are the most commonly used anti-cholesterol drugs. There are several reasons why

we study this market. First, there are large amounts of publicity surrounding this class of drugs, much

of it related to post-marketing clinical studies (Sillup and Porth, 2008). Second, it is plausible that

patients and physicians become informed about their efficacy and side-effects through the media that

they have access, e.g., newspapers, professional magazines, etc. Third, this is an important market for

public health and firms’ profitability. In the past decade, the most common cause of death is heart

disease.2 Medical research has consistently found a positive correlation between high cholesterol levels

and risks of coronary heart disease. Because statins are effective in lowering cholesterol levels, it has

been viewed as a way to reduce risks of heart disease and its demand has expanded very rapidly. From

the public policy standpoint, it is useful to know if media coverage would lead physicians to choose a

drug that has the strongest clinical evidence of reducing risks of heart disease at the time. From the1Principle of Marketing 7th edition, Chapter 15, Philip Kotler, Gary Amstrong and Peggy H. Cunningham2Public Health Agency of Canada ( http://www.phac-aspc.gc.ca/publicat/lcd-pcd97/table1-eng.php )

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firms’ standpoint, understanding how the demand side perceives publicity could also help them design

their marketing campaign more efficiently.

Previous work has classified news stories as positive, negative, or neutral in a single dimension

(Berger et al., 2010; Chintagunta et al., 2009; Goldenberg et al., 2007; Huang and Chen, 2006). We argue

that such a single-dimensional classification could be misleading. For example, a news article might

report that an anti-cholesterol drug lowers cholesterol levels more effectively than do its competitors, but

that some patients experience serious side-effects. This article could be coded as positive, negative, or

neutral in a single-dimensional classification depending on the reader’s perspective. To avoid ambiguity

of context, we code the information of an article into a multi-dimensional attribute space. We consider

three attributes for our drugs: (i) their ability to lower cholesterol levels (short-term efficacy); (ii) their

ability to reduce the risks of heart disease (long-term efficacy); (iii) their side effects. Continuing with

the above example, our algorithm would classify the article as positive in the dimension of lowering

cholesterol levels and negative in the dimension of side effects. This multi-dimensional coding scheme

can reduce measurement errors made in single-dimensional coding schemes used in the previous studies.

More importantly, it allows us to measure how different types of information may affect consumer

demand. To the best of our knowledge, our research will be the first to use publicity data that is coded

in such a precise fashion.

We implement our methodology to code the publicity data that are accessible in Canada. In addition

to the publicity data, we have collected data on sales, detailing, journal advertising and clinical trial

outcomes from 1993 to 2004. One potential advantage of using the data from the Canadian market

is that direct-to-consumer advertising for prescription drugs is strictly regulated. While firms are

restricted in their use of DTCA, they may have a stronger incentive to communicate directly with

patients through publicity (e.g., by running more press releases). Therefore, the Canadian market may

provide a more interesting setting for studying the roles of publicity.

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We incorporate our multi-dimensional measure of publicity into a demand system generated from

a standard discrete-choice demand model a la Berry (1994). Consumer utility functions over drugs can

be written as a function of product characteristics, including scientific evidence, publicity and other

marketing activities. Market shares are then determined as the aggregate outcome of consumer utility

maximizing decisions. Moreover, prices of prescription drugs are regulated in Canada. As a result,

prices change very infrequently over time. This institutional setting has also made it easier for us to

focus on studying how marketing communication activities would affect demand.

Our results suggest that not all forms of publicity are equal. We find that comparison articles

are much more effective than non-comparison articles. Moreover, we find that the most effective type

of publicity is to provide information on the cholesterol-lowering ability of the drug, and the second

most effective type of publicity is about side-effects. Surprisingly, despite the ultimate goal of taking

anti-cholesterol medication is to reduce heart disease risks, publicity related to this dimension has very

little impact on demand for a particular drug. We also find evidence that physicians/patients may

generalize clinical evidence on individual drug specific ability to reduce heart disease risks to the whole

class of statins. The results suggest that manufacturers of the drugs which are more potent in reducing

cholesterol could free-ride on the heart disease clinical evidence for other drugs. Our results could help

firms decide which types of post-marketing clinical trials to invest.

The rest of this paper is organized as follows. Section 2 reviews previous literature. Section 3

describes background information including the Canadian health care system, regulations on the phar-

maceutical industry, and the market for statins. Section 4 summarizes how we collect and code the

publicity data. Section 5 reports the estimation results. Section 6 is the conclusion.

2 Literature Review

Most of the previous empirical studies on publicity focus on studying the impact of critics and product

reviews on demand instead of media coverage (Basuroy et al., 2003; Berger et al., 2010; Chevalier and

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Mayzlin, 2006; Godes and Mayzlin, 2004). The results of these studies appear to be ambiguous. While

some studies indicate that positive (negative) information increases (decreases) product evaluation

(Goldenberg et al., 2007; Huang and Chen, 2006), others find evidence that publicity (product reviews)

might increase sales of the product regardless of whether it is positive or negative (Berger et al., 2010).

Our research is related to the growing pharmaceutical marketing literature, which focuses on study-

ing the roles of detailing, journal advertising, DTCA and scientific evidence (Azoulay, 2002; Berndt

et al., 1997; Calfee et al., 2002; Chan et al., 2010; Ching and Ishihara, 2010b; Cockburn and Anis,

2001; Iizuka and Jin, 2005; Liaukonyte, 2009). This literature seldom investigates the roles of media

coverage. As far as we know, Chintagunta et al. (2009) is the only study that incorporate data on

news coverage when estimating the demand for pharmaceutical products. However, their focus is to

study learning through consumption experiences, and hence they simply incorporate publicity data as

a control variable in their analysis. They classify publicity solely based on the titles of articles, and

find puzzling results on the effect of their publicity variable. In particular, they find that news articles

have a positive influence on prescription choice, regardless how the title sounds (positive, neutral or

negative). They admit that this could be due to measurement error in their raw data or problems in

the data coding design. Our coding methodology could potentially address the shortcomings of the

literature mentioned above.

We should point out that our method is also related to the one proposed by Liaukonyte (2009), who

analyzes advertising content by classifying them as comparative and non-comparative advertising in

the over-the-counter analgesics market. She finds evidence that the impact of comparative advertising

on demand is more effective but shorter-lived than that of non-comparative advertising. However, for

each classification, she still follows the traditional method by coding each article as positive, negative

and neutral in a single dimension. Our multi-dimensional method of classification could provide richer

results than does her approach.

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3 Background

3.1 Health Care System in Canada

The Canada Health Act adopted in 1984 entitles all legal Canadian residents to receive medically nec-

essary services without co-payment if the services are provided in hospital, or by practitioners.3 While

Canada’s public health system covers drugs administered in a hospital, it does not cover outpatient

prescription drugs costs, except for indigenous persons (i.e., First Nations and Inuit peoples), Canadian

Forces members, veterans, Royal Canadian Mounted Police members, and inmates of federal peniten-

tiaries. Moreover, provinces and territories administer publicly financed program to provide prescription

drug coverage concentrated on seniors, disabled citizens, and low-income persons with special needs.

In 2004, according to the Auditor General of Canada, about one million Canadians were eligible for

federal drug benefits and more than nine million were covered by provincial plans. About two-thirds

of Canadian residents are covered for prescription drugs by private insurance obtained through their

employer or purchased individually. Consequently, according to estimates for year 2000, 98% of the

Canadian population has some form of public or private sector drug plan coverage that provides a

degree of protection against severe drug expenditures (Fraser Group and Tristat Resources, 2002).

3.2 Regulation of Price and Direct-to-Consumer Advertising for Prescription Drugsin Canada

In Canada, prices of patented prescription drugs are strictly regulated. Health Canada introduced a

government agency, the Patented Medicine Prices Review Board (PMPRB) through amendments to the

Patent Act in 1987. This board regulates drugs that are still under patent and which have no generic

substitutes by establishing the maximum prices that can be charged in Canada for them (Anis and

Wen, 1998; Paris and Docteur, 2006). The PMPRB uses the term, “excessive” price, to characterize

either a high introductory price of a new medication, or a substantial increase in the price of an existing

medication. The essence of these regulations can be summarized as follows:3Health Canada http://www.hc-sc.gc.ca/hcs-sss/medi-assur/cha-lcs/index-eng.php

5

1. For a new strength or a new dosage form of an existing medicine, the price is considered excessive

if it does not possess a reasonable relationship to the average price of the existing medicine.

2. For a drug that represents a therapeutic breakthrough or provides a substantial improvement over

comparable existing medicines, the price is excessive if it exceeds the prices of all the comparable

products in the therapeutic class and the median of the prices in seven countries: France, Germany,

Italy, Sweden, Switzerland, the UK and the U.S.

3. For a drug that provides moderate, little or no therapeutic advantage over comparable medicines,

the price is judged excessive if it exceeds the price of comparable products in the Canadian market.

PMPRB may use the median of the prices in seven countries as a reference when it is impossible

or inappropriate to identify comparable drugs in Canada.

4. The change in price of existing medications is to be considered excessive if the price increase

exceeds the increase in the general consumer price index.

DTCA is also regulated under Canada’s Food and Drugs Act and Regulations. Health Canada cur-

rently allows two forms of advertising: (i) reminder advertisements, which include only the brand name

and no health claims or hints about the product’s use and (ii) disease-oriented or help-seeking advertise-

ments, which do not mention a specific brand but discuss a condition and suggest that consumers ask

their doctor about an unspecified treatment (Gardner et al., 2003). Because of the extreme regulation,

pharmaceutical industry in Canada merely spent CAD 22 million in 2006 on DTCA (Mintzes et al.,

2009) compared to USD 4.2 billion in 2005 in the US, where DTCA restriction was relaxed in 1997.

3.3 The Market for anti-cholesterol drugs – Statins

There are two main types of cholesterols: LDL (the “bad” cholesterol) and HDL (the “good” choles-

terol). High amounts of the bad LDL will deposit cholesterol on the artery walls forming plaques.

More and more plaques will narrow the arteries lumen and may eventually block blood flow, and lead

6

to a heart attack. On the other hand, the good HDL takes excess cholesterol away and carries it

back to the liver to be excreted. It can also remove some of the cholesterol already attached to the

artery walls. Statins are a class of most commonly used anti-cholesterol drugs. They lower the level of

LDL by blocking HMG-CoA reductase, which is the enzyme that synthesizes cholesterol in the liver.

Clinical research results have suggested that statins can also raise HDL by 5 to 10 percents. However

some medical researchers argue that such modest increase may not have a significant positive effect in

reducing the risks of heart disease.4 Table 1 contains a brief synthesis of the main descriptive statistics

for the seven statins. In 1978, scientists at Merck identified the first statin, lovastatin, which hit the

market in 1987 under the brand name Mevacor after the long process of clinical trials needed for FDA

approval. To market statins effectively, Merck needed to convince patients/physicians that increase in

cholesterol levels is associated with cardiovascular diseases, and statins are safe and effective in reducing

LDL. Their marketing efforts, together with the government public awareness campaign, have made

the public to become more familiar with the potential risk of high cholesterol levels.5

In 1991, a rival drugmaker Bristol Myers Squibb introduced the second statin, Pravachol (pravas-

tatin). To compete against this new statin and to convince physicians of the long-term benefits of

statins, Merck introduced a new statin, Zocor(simvastatin) and sponsored a clinical study called 4S.

From the results of 4S published in 1994, researchers announced the findings that Zocor not only lowers

patients’ cholesterol levels but also reduces their risks of heart disease. With the help of the successful

clinical results, Zocor quickly became the number one seller in this market.

In 1985, researchers in Warner-Lambert succeeded to synthesize a different statin, Lipitor (atorvas-

tatin). To catch up the existing statins, Warner-Lambert signed an agreement to co-promote Lipitor

with Pfizer, the fifth largest drugmaker. In 1996, Pfizer announced results from a head-to-head clinical

trial called CURVES. The results showed that Liptior was more effective in lowering cholesterol lev-4THE MEDICAL NEWS (http://cme.medscape.com/viewarticle/479499 5)5CNN Money (http://money.cnn.com/magazines/fortune/fortune archive/2003/01/20/335643/)

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els than the four older statins: Merck’s Zocor and Mevacor, Novartis’s Lescol, and BMS’s Pravachol.

However, this study does not establish any direct evidence on whether Lipitor (atorvastatin) can lower

the risks of heart disease. When Lipitor hit the market in 1997, Pfizer focused on communicating its

superior efficacy in lowering cholesterol levels with physicians. Interestingly, even though Lipitor did

not have a clinical trial that provided direct evidence that it can reduce risks of heart disease until

2001, its market had expanded steadily and rapidly since its introduction.6 In 2002, Lipitor achieved

estimated sales of $ 7.4 billion and became the best-selling product in the prescription drug market.

Not all statins were as successful as Lipitor and Zocor. In 1998, Bayer introduced the sixth statin,

Baycol (cerivastatin) and a clinical study sponsored by Bayer showed Baycol’s impressive effectiveness

in lowering cholesteorol levels.7 However, Public Citizen, an influential Washington-based consumer

advocacy group, issued a petition to FDA for the removal of Baycol claiming that Baycol had fatal side-

effects including Rhabdomyolysis, the rapid breakdown of skeletal muscle, and resultant renal failure.

In the U.S., 52 deaths were reported in patients using Baycol and so Bayer “voluntarily” withdrew

Baycol from the market worldwide in 2001.

In 2003, AstraZeneca released Crestor (rosuvastatin), a seventh statin, and claimed that it would

lower cholesterol levels far more effectively than other statins including Lipitor. An AstraZeneca spon-

sored head-to-head clinical study called STELLAR, provides clinical evidence that Crestor not only

lowers LDL (bad cholesterol) levels but also increases HDL (good cholesterol) levels significantly more

than does Liptior. However, in less than a year since the drug has first been marketed, there are

some cases of adverse reactions (more precisely, acute renal failure), which prompted Public Citizen

to petition for the removal of Crestor from the market. The FDA issued several warnings concerning

Crestor related side-effects, but it eventually declined the petition and concluded that it is not riskier6A positive correlation between high cholesterol levels and risks of coronary heart disease has been found in medical

research. Nevertheless, a drug that can lower cholesterol levels effectively does not necessarily mean that it can reduce therisks of heart disease. For instance, a recent clinical trial shows that a new anti-cholesterol combination drug, Vytorin,does not reduce the risks of heart disease even though it is very effective in lowering cholesterol levels (Park, 2008).

7http://www.pslgroup.com/dg/5b8a2.htm

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than other statins on the market. Although safety issues for Crestor have been raised, Crestor has

experienced strong growth in the pharmaceutical market. We next turn to describe our data.

4 Data

The analysis integrates three different data sources: (i) publicity data covering statins derived from

Factiva, (ii) product level monthly prescription volume, detailing and journal advertising data for the

Canadian statin market from IMS Canada, and (iii) landmark clinical trial data for statins.

4.1 Publicity Data

To investigate the impact of publicity on consumer demand, we collect publicity data covering statins

from 1986 to 2004 from Factiva. Factiva is a division of Dow Jones & Company that provides access

to more than 25,000 authoritative sources such as newspapers, journals, magazines, news and radio

transcripts, etc. We search for articles that contains a molecule name (i.e., atorvastatin, cerivastatin,

fluvastatin, lovastatin, pravastatin, rosuvastatin and pravastatin), and/or a brand name (i.e., Lipitor,

Baycol, Lescol, Mevacor, Pravachol, Zocor and Pravachol) of statins. We collect 37,115 articles in

the period of year 1986 to 2004 from Factiva. Table 2 presents the summary of sources in the our

database and the number of articles covering statins for each category. Then, we restrict the sample

to articles from Canadian Accessible Sources, to which Canadian physicians or patients may have

access. These sources include trade/professional magazines, academic medical journals, online sources,

Canadian television news, Canadian newspapers, and Canadian magazines, as well as U.S. television

news from four major television networks (ABC, NBC, CBS and FOX) and CNN, the eight biggest U.S.

newspapers with more than 500,000 daily circulations and the 25 top selling U.S. magazines. We assume

that the public might not have direct access to the press release from news agencies. Therefore, we leave

out articles from news agency such as Agence France-Presse, The Associated Press and Reuters. As a

result, we include 7,002 articles in the analysis. For each article, we extracted data such as headline,

9

source, contents and publication date. We detail our data collection approach in the appendix.

Table 3 presents the number of articles and their sources covering statins by year. The number of

articles covering statins in the Factiva database varies, mostly increases, in each year. For example, in

our Canadian sample, the number of articles (no. of sources) is 4 (3) in year 1986, and it increases to

2,688 (168) in year 2004. The number of sources covering statins also increases. There are two possible

reasons for the increase in the number of sources over time. First, it could be that the actual sources

have covered statins more than previous years and so Factiva has added more articles to its database

and it leads to increased number of sources. Second, Factiva might include more sources in its online

database. If the latter explains the increase in the number of sources across years, we need to weight

the each article by the number of sources available in each year. Therefore, we collect information on

when each source was included and excluded in Factiva database, and obtain the number of Canadian

sources available in the Factiva database over time. We find that the trend of the total number of

sources is similar to that for We weigh each article by dividing the number of sources available when

the article was published.

To assess the impact of publicity on consumer demand, it is essential to construct appropriate

measures of publicity. Since we often encounter articles which contain more than one message. Since

an article can be positive in one dimension but negative in another for a drug, simply coding its overall

tone as positive, neutral, or negative is likely to lead to ambiguity or measurement error. Therefore, we

interpret each article along multiple dimensions: (i) the ability of the drug to lower cholesterol levels

(short-term efficacy), (ii) the ability of the drug to reduce heart disease risks (long-term efficacy), and

(iii) the drug’s side effects. For each dimension, we label it as comparison if the article compares at

least two statins, and non-comparison otherwise.

For non-comparison articles, we score each article using a three-step Likert scale (+1, 0, -1) to

assess the positive, neutral or negative tone of the article, we assign “+1” (“-1”) if the article favors

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(does not favor) the drug, and “0” if the article shows a neutral attitude toward the focal drug for each

dimension. For example, “Lipitor reduces cholesterol levels fast,” is counted as “+1” in the dimension

of short-term efficacy, “Baycol can cause a fatal damage to patients’ kidney” is considered to be “-1”

in the dimension of side effects, and “Pravachol was well-tolerated” is considered to be “+1” in the

dimension of side effects.

The coding of comparison articles is somewhat more complicated. In order to capture all of the

information contained in the message we score a comparison article according to the sum of its absolute

and relative attitudes towards each drug cited in the article. For the absolute score, we assign each

drug “+1”, “0”, or “-1” if the article shows a positive, neutral, or negative attitude towards the focal

drug, respectively, as we do for non-comparison articles. For the relative score, we assign the focal drug

the number of drugs that are reported inferior to the focal drug in the article. To illustrate how our

algorithm works, we provide the following examples:

1. When an article reports that both Lipitor and Zocor lowered cholesterol levels but Lipitor did

so more quickly than Zocor. For the absolute score, since the article states that both drugs are

effective in lowering cholesterol levels (short-term efficacy), both of them are assigned “+1”. For

the relative score, since the article reports that Lipitor is more effective than Zocor, Lipitor is

assigned “+1”. Therefore, we code Lipitor as “+2” = 1 (absolute) + 1 (relative), and Zocor as

“+1”= 1 (absolute) + 0 (relative) in the dimension of short-term efficacy.

2. When an article reports that Lipitor, Zocor and Pravachol were well-tolerated among patients

in a clinical trial. Patients with Pravachol showed no significant side effects and less patients

experienced side effects with Lipitor than with Zocor, we code Pravachol as ”+3” = 1 (absolute)

+ 2 (relative), Lipitor as “+2” = 1 (absolute) + 1 (relative), and Zocor as “+1” = 1 (absolute)

+ 0 (relative) in the dimension of side effects.

Table 4 presents a descriptive summary of publicity variables. While LC (Lowering Cholesterol

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levels) dimension articles are often comparison, RH (Reducing risks of Heart disease) articles are mostly

non-comparison articles.

4.2 Prescription Volume, Detailing and Journal Advertising Data

The product level data obtained from the market research firm, IMS Canada, consist of monthly

observations of prescription volumes, detailing costs, and journal advertising pages for each statin

across Canada from March 1993 to December 2004. The market is defined as the national market for

month t, where t = 1, · · · , 130. The observation is defined as a molecule-month combination.

In figure 1, we plot the monthly prescription size for the four leading statin drugs (Lipitor, Zocor,

Pravachol and Crestor) in Canada. The prescription size for Lipitor reached almost one million by

2001 while the earlier arrivals, Zocor and Pravachol, had 300,000 and 150,000 monthly prescriptions,

respectively. Figure 2 shows the evolution of log of market shares for leading drugs relative to the

outside good.

Previous research has documented that marketing activities have an influence on the prescription

choices of physicians. To control for the impact of detailing and journal advertising, we incorporate

detailing spending and journal advertising pages for each drug. To convert nominal to real dollars of

detailing, we use Consumer Price Index from Statistics Canada. Our dataset does not include the period

when Mevacor, Pravachol or Zocor was introduced. In 1993, these three drugs might have accumulated

goodwill stocks of detailing and journal advertising efforts. To control the initial condition problem, we

follow Ching and Ishihara (2010b) and assume that before March 1993, these three drugs would have

spent the same monthly detailing and journal advertising efforts as the average of detailing and journal

advertising spending between Mar 1993 and Feb 1994. Figures 5 and 6 graph the monthly detailing and

journal advertising stocks for four leading drugs. The market entries of Lipitor and Crestor coincide

with large detailing and journal advertising efforts. Zocor and Pravachol stopped detailing and journal

advertising efforts when the generic products were introduced in the market.

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The demand system used here will include an outside good (i.e., we allow patients with high LDL

to choose treatments other than statins, or no treatment at all). We therefore need to calibrate the

potential market size for statins, which includes high LDL patients who are on statins and other anti-

cholesterol drugs, and those who choose not to take any drugs. In order to estimate what percentages

of Canadians have a high cholesterol problem, we use data from the Canadian Heart Health Survey,

recorded between 1986 and 1992. The study suggests that 33% of the total Canadian population aged

16 to 65 and 85% of that over 65 have a high cholesterol (i.e., high LDL) problem. We multiply this

number by total Canadian population for each age group in a given month, as defined by the Statistics

Canada, and use the result as a proxy for the total number of potential patients for statins. In order

to convert total population with high cholesterol problem into the number of prescriptions, we assume

that each patient visits a physician and receives a prescription once per 90 days. Based on this measure

of potential market size, we calculate each brand’s and outside good’s market shares in each month.

For most product categories, price is an important factor that affects consumers’ purchase decision

on the product. However, we do not include prices in our demand system because the institutional

details of Canada suggest that it may not play an important role. Price regulation for prescription

drugs in Canada often leads to infrequent price changes. Table 6 shows the price which the province of

Ontario pays for Ontario Drug Benefit program beneficiary. The table suggests that prices for statin

have hardly changed over time, and this should alleviate the concern of price endogeneity (the data

suggests that price is hardly correlated with unobserved demand shock that varies over time), and

including brand fixed effects in our model should suffice in capturing the impact of prices. Moreover,

around 98% of Canadian residents possess a certain type of coverage for prescription drugs.

4.3 Landmark Clinical Trials

Azoulay (2002), Ching and Ishihara (2010a) and Cockburn and Anis (2001) find evidence that clinical

trials have a significant impact on physicians’ prescription decisions. Hence, to control for the impact

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of clinical evidence, we collect landmark clinical trial data from the U.S. National Library of Medicine

(www.medscape.com) and include indicators for each one in our empirical analysis. Table 7 lists the

landmark trials we include in the analysis. We simply include the clinical trial dummies which become

one after the trial has been released. The dummies only apply to the focal drugs in the clinical trials.

For instance, the REVERSAL study published in March 2004 favors Lipitor over Pravachol. We create

a dummy variable for REVERSAL Lipitor equal to one when the observation for Lipitor is post-2004

and zero otherwise.

5 Econometric Model and Results

5.1 Model

We adopt the discrete-choice approach of Berry (1994) to model the demand for statins. This approach

offers several benefits: the derived demand equations are consistent with consumer theory and the

entry of new products can be easily incorporated. Consumer utility functions over drugs can be written

as a function of product characteristics, including scientific evidence, publicity and other marketing

activities. Market shares are then determined as the aggregate outcome of consumer utility maximizing

decisions. We define the utility of a patient i when she buys an anti-cholesterol drug j at time t as

follows:

Uijt = STK Xjt · β +17∑

k=1

Landmarkkjt · θk +7∑

k=1

γj + ξjt + εijt, (1)

where STK Xjt = {STK Publicityjt, STK Detailingjt, STK JournalADjt} represents a vector of

goodwill stock of publicity, detailing and journal advertising, respectively; Landmarkkjt denotes a

dummy variable for landmark trial k for drug j at time t (Landmarkkjt is equal to one if drug j receives

a landmark trial k favoring its efficacy before time t, and zero otherwise); ξjt may be interpreted as

the mean of the consumer valuation of an unobserved product characteristic; εijt is assumed to be i.i.d.

extreme value distributed with mean zero across consumers. We normalize the mean utility of the

outside good (j = 0) to be zero, and hence Ui0t = εi0t.

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Detailing and journal advertising have been known to have long lasting impacts on demand (Azoulay,

2002; Berndt et al., 1997). Therefore, in explaining current period sales, stocks of cumulative detailing,

journal advertising, and publicity are more appropriate measures than flows. Following Azoulay (2002),

we allow for the possibility that the stock variables decay over time with a monthly carryover rates of

δ = .95. For example, the stock variable of detailing is defined as:

STK Detailingjt = δ · STK Detailingjt−1 + Detailingjt. (2)

Since we assume that error terms εijt follow the extreme value distribution, the market share of

drug j at time t is given by the logit formula:

Sjt =exp(STK Xjt · β +

∑19k=1 Landmarkkjt · θk +

∑7k=1 γj + ξjt)

1 +∑7

j=1 exp(STK Xjt · β +∑19

k=1 Landmarkkjt · θk +∑7

k=1 γj + ξjt). (3)

It follows that,

log(Sjt

St) = STK Xjt · β +

19∑

k=1

Landmarkkjt · θk +7∑

k=1

γj + ξjt, (4)

where Sjt is the market share of drug j; ξjt are the unobserved characteristics of drug j at time t and

become the error terms. Detailed descriptions of included variables are listed in each table of results.

5.2 Endogeneity of Detailing and Journal Advertising

The previous literature has recognized that detailing and journal advertising could be endogenous. A

firm’s detailing and journal advertising effort might be correlated with shocks to its drug’s expected

quality over time, which are unobservable to researchers. If a manufacturing firm observes these shocks,

it is likely to choose a level of detailing and/or journal advertising to reflect the shock. Hence, if

researchers do not incorporate the shock into the model, the coefficients on the detailing and journal

advertising variables will be biased. We assume that clinical trial results and publicity are the major

sources of information about the quality of drugs to consumers and physicians. To control for the

unobserved shock to expected quality, we collect all the clinical trial data for statins between 1993

15

and 2004 and publicity data between 1986 and 2004, and incorporate them in the analysis. By doing

so, we attempt to control for the endogeneity problem of detailing and journal advertising caused by

unobserved shock to drugs’ expected quality.

5.3 Is Publicity Exogenous?

Figures 3 and 4 present three major statins’ (Liptor, Pravachol and Zocor) stock of LC (Lowering

Cholesterol levels) comparison score and RH (Reducing risks of Heart disease) non-comparison score,

respectively. The graphs suggest that the LC comparison score and RH non-comparison score of Lipitor

is highly correlated. However, Pravachol and Zocor have show different patterns in LC comparison levels

and RH non-comparison levels. This will allow us to disentangle the effects of two different types of

publicity.

There may also be some concern that publicity itself is endogenous. This is particularly true for

articles that discuss sales of the drug. In other words, publicity might be correlated with unobserved

shocks affecting sales of the drug. To address this concern we do not include articles purely related to

sales of drugs (e.g., an article which reports that the sales of Lipitor have improved from the previous

year) since we expect that the more popular drugs would have more meida coverage which emphasizes

sales of the drug. The articles we include in our analysis refer to efficacy and side effects of statins and

this type of publicity is generally yielded by the release of clinical trial results. In some broad economic

sense, one can argue that these clinical trial results are correlated with unobserved shocks of sales over

time. However, considering the time needed for publication and uncertain outcomes, we consider these

clinical trial results and publicity as being uncorrelated with month-to-month changes in unobserved

shocks.

5.4 Results

Table 8 reports the estimation results of Equation 4 [only use the log regressions]. We take log of

all the stocks of marketing and publicity variables when running these regressions. The literature

16

has consistently found evidence that drugs are experience goods (Crawford and Shum, 2005; Ching,

2010a,b; Ferreyra and Kosenok, 2010), and detailing activities contain information which helps the

demand side to learn the true quality of the products (Ching and Ishihara, 2010b,a; Leffler, 1981;

Narayanan et al., 2005). When patients/physicians slowly learn the true quality of the products from

consumption experiences, it is plausible that the marginal return of marketing activities would diminish

over time. Our prior is that publicity serves a similar purpose, and hence it seems likely that its effects

would also diminish over time. By taking logs of these variables, we are able to capture this feature.

The first specification only includes stocks of detailing and journal advertising. It does not include

any publicity variables. This regression serves as a benchmark for comparison. Consistent with the

previous literature, we find that the effect of the detailing stock is positive and statistically significant.

The stock of journal advertising is much weaker - in fact, it is not significant.

The second specification includes the “traditional” type of publicity variables, where we do not

break down the information content into a multi-dimensional attribute space. Instead, we simply

have two variables: (i) comparison articles, and (ii) non-comparison articles. Under this traditional

scheme, we code one article as positive (+1) when the sum of the three-step Likert scales across three

dimensions is greater than zero, or negative (-1) when the sum is smaller than zero. These variables

mimic the previous approach which codes articles in a single dimension fashion. The results show that

the effect of the stock of comparison articles is positive and significant, while the effect of the stock

of non-comparison articles is insignificant. Interestingly, the effect of the stock of journal advertising

now becomes positive and significant. This may indicate that the first regression suffers from omitted

variable bias.

In the third specification, we consider publicity about a drug along three dimensions: (i) its ability

to lower cholesterol levels (LC); (ii) its ability to reduce the risks of heart disease (RH); (iii) its side-

effects (SE). We further distinguish whether the publicity is comparison or non-comparison, except

17

for RH messages. For RH messages, we only consider non-comparison articles because the number

of comparison articles is close to zero for RH. This is because there are very few (landmark) clinical

trials which conduct head-to-head comparisons in this dimension. Although the ultimate goal of taking

statins is to reduce risks of heart disease, it is possible that patients/physicians might mainly focus

on the ability of drugs to lower cholesterol levels because it is well-documented that LDL and heart

disease rates are positively correlated. It is important to empirically investigate whether this is the case

because of its managerial and public policy implications. Our results show that the effect of the stock

of LC comparison articles is positive and significant, while the effect of LC non-comparison articles is

not significant. Interestingly, the effect of the stock of RH is not significant. The results support our

hypothesis that patients/physicians mainly pay attention to the LC dimension. We also find that the

effect of the stock of SE comparison articles is positive and significant. However, we find that the effect

of SE non-comparison articles is negative and significant, which is counterintuitive.

Regarding LC comparison articles, we find that many articles are consistent in reporting the relative

strength of statins in lowering cholesterol levels. It is common for articles to compare Lipitor with

Mevacor, Lescol, Pravachol and Zocor - all of them are not as potent as Lipitor. Even though we have

given Lipitor an extra unit increase when coding such articles,8 it might not be enough to reflect the

additional benefits that Lipitor gets if we restrict the coefficient for LC comparison to be the same

across drugs. Moreover, Baycol and Crestor are more potent than Lipitor, and they are introduced

after Lipitor has been on the market for 1 year and 6 years, respectively. It turns out that most of

the comparison articles involving Baycol and Crestor are typically comparing it with Lipitor. After

reading these articles, it seems likely that at least some of the patients/physicians would infer that

Baycol or Crestor is better than other statins as well. Since our coding scheme does not reflect this,

the effect of LC comparison articles for Baycol and Crestor may be stronger than that for Lipitor.

Based on this intuition, we also allow LC comparison to have heterogeneous effects across drugs in the8When two drugs are compared, the more potent one receives +1 and the less potent one 0 for the measure of relativity.

18

fourth specification. The estimation results are consistent with our intuition. The point estimates of LC

comparison are all positive and highly significant. The coefficient of Crestor and Baycol are close to each

other, and higher than Lipitor. The differences are statistically significant. Moreover, the coefficient

of Lipitor is much higher than those of other statins. Interestingly, the sign of the SE non-comparison

variable now switches from negative to positive, and is statistically significant. This suggests that

its negative coefficient in specification 3 could be due to misspecification bias. Nevertheless, the RH

non-comparison variable remains insignificant.

In the medical literature, researchers believe that high cholesterol levels are an indicator for high

risks of heart disease. A drug that can lower cholesterol levels would likely reduce risks of heart disease.

However, without a clinical trial that establishes a direct causal effect of taking the drug on heart attack

incidence rates, its manufacturer is not allowed to directly tell doctors or advertise that the drug can

reduce the risk of heart attack. Consequently, some drug companies are willing to sponsor landmark

clinical trials to establish such evidence. Most of the RH articles report on clinical trial results. We

therefore find it surprising that RH non-comparison variable is not significant. By investigating the

medical continuing education literature, we find evidence that many physicians may not strictly follow

evidence-based prescribing practice (Mamdani et al., 2008). It appears that some physicians may

assume a drug’s ability to lower the LDL level will likely translate to reducing heart disease risks and

ignore the possibility that it may have other side-effects that work the other way. This could explain

why the RH non-comparison variable is insignificant after controlling for LC.

Another possibilty is that physicians and patients generalize the ability of one drug to reduce heart

disease risks to the whole statin class. In order to test this hypothesis, we estimate specification 5 where

we create RH sum variable by summing RH non-comparison across drugs, taking the log, and allowing

it to interact with the LC comparison variable. The interaction term is positive and significant, which

supports our hypothesis. In specification 6, we allow the effects of the interaction term to differ across

19

drugs. Because of the multi-collinearity issue, we do not allow the LC comparison variable to have

heterogeneous effects across drugs. These interaction terms are all positive and significant except for

Pravachol. Overall, the results from specification 6 are consistent with those of 4 and 5.

Previous studies that have investigated the impact of marketing-mix on sales in this market usually

do not use clinical trial outcomes or publicity data with the exception of Azoulay (2002) who focuses

on clinical trial data. But even Azoulay (2002) does not use publicity data. The missing clinical trials

and publicity information in these studies may lead to the potential endogeneity problem of detailing

or prices. The typical remedy that these studies use is the instrumental variable approach. In our case,

we believe that the endogeneity problem is less of a concern. Pharmaceutical prices are regulated in

Canada and cannot be freely changed by firms. Moreover, we have included more information than

what the previous studies did in our regressions. In particular, this is the first study that measures

publicity in a multi-dimensional way. We therefore should alleviate the endogeneity problem of detailing

due to omitted variables compared with previous studies.

We should also note that the correlation between journal advertising and detailing is quite high. The

correlations are 0.824 and 0.721 for their flow and stock variables, respectively. Such high correlations

suggest that it could be hard to separately measure their effects (Berndt et al., 1997). This could

explain why the point estimates of the stock of journal advertising vary across specifications. Since the

estimate of the stock of detailing seems to be more stable. As a robustness check, we have run another

set of regressions where we drop the stock of journal advertising. Most of the results reported above

are robust in this specification. The results of this set of regressions are reported in tables 12 and 13

in the appendix.

Venkataraman and Stremersch (2007) and Ching and Ishihara (2010a) find evidence that new in-

dications or clinical evidence could change the effectiveness of detailing. We therefore consider an

alternative specification where we allow the stock of detailing to interact with the clinical trial dum-

20

mies. This allows us to capture the idea that the effect of detailing could be heterogeneous across drugs.

We find that our results regarding publicity are largely robust in this specification as well. The results

of this set of regressions are reported in tables 14 and 15 in the appendix as well.9

5.5 Do firms view publicity as substitute/complement for detailing and journaladvertising?

As we mentioned in the introduction, publicity might serve as a substitute for detailing and/or journal

advertising. As a test of whether this is the case, we regress detailing/journal advertising on the

publicity variables. The results are reported in tables 9 and 10. Under specification 2 in table 9 for the

detailing regression, we find that LC comparison and RH non-comparison variables are both positive

and significant. In table 10 for the journal advertising regression, we find that the LC comparison

variable is also positive and significant. If firms treat publicity as a substitute for detailing/journal

advertising, we would expect that the coefficients for these variables should be negative. The positive

coefficient could be due to the fact that most of the media coverage reports some new clinical findings.

As argued in the previous literature (Azoulay, 2002; Ching and Ishihara, 2010b), the effectiveness of

detailing/journal advertising may increases with positive clinical outcomes, and as a result, firms may

also do more detailing. This may lead to the positive coefficients in these regressions.

We therefore run two more regressions for detailing/journal advertising response functions by con-

trolling for the impact of landmark clinical trials. In specification 3 in tables 9 and 10, we simply

include the clinical trial dummies which become one after the results from the trial have been released,

but the dummy only applies to the focal drug. However, a clinical trial about drug A (say Lipitor)

could affect the marketing activities for other drugs as well. Conceivably, if drug A receives good news,

the marginal return of detailing for the rival drugs might decrease as it now becomes harder to convince

doctors to switch away from drug A. We therefore allow the clinical trial dummies to affect drugs other9We have also considered to separate SE comparison and non-comparison, and RH non-comparison variables by drug.

However, the number of observation becomes quite small and the individual effects of these variables cannot be reliablyestimated.

21

than the ones being investigated in the trial. The results are reported in specification 4 in tables 9

and 10. In both specifications 3 and 4, we see that the publicity variables are no longer significant.

The results confirm our conjecture above. Moreover, they also indicate that firms’ detailing or journal

advertising decisions may not be influenced by publicity in general.10 The coefficients for clinical trial

dummies for both detailing and journal advertising estimating functions are reported in table 16 in the

appendix.

6 Conclusion

This research attempts to study the role of publicity on the choice of prescription drugs. In the study

we employ rich data sets to reduce a bias caused by omitted variables affecting the prescription choices.

The result presented here demonstrates that product market in the statin class is shaped by firms’

detailing, journal advertising, publicity and scientific evidence.

By allowing the information that is provided in the text of publicity the model is able to show

that different types of publicity have different impact on the choice of prescription drugs. The results

also raise the interesting question of the extent to which publicity is a strategic instrument available to

pharmaceutical companies. We will investigate this further in the future.

10It is important to note that our analysis is at the product level. It is possible that firms’ decisions to detail mightchange at the physician level in response to publicity Chen and Tan (2010).

22

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24

Table 1: Summary of Statins

Lipitor atorvastatin Apr-1997 2010(Exp.) 2011(Exp.) Pfizer 20mg

Baycol cerivastatin Mar-1998 N/A N/A Bayer 0.2mg

Lescol fluvastatin Mar-1994 2011(Exp.) 2012(Exp.) Novartis 60mg

Mevacor lovastatin Sep-1987 Apr-1997 Jun-2001 Merck 45mg

Pravachol pravastatin Nov-1991 Aug-2000 Apr-2006 Bristol-Myers Squibb 30mg

Crestor rosuvastatin Sep-2003 2012(Exp.) 2013(Exp.) AstraZeneca 10mg

Zocor simvastatin Feb-1992 Mar-2003 Jun-2006 Merck & Co. 30mg

* Defined Daily Dose (DDD) is the assumed average maintenance dose per day for a drug used for its main

indication in adults.

DDD*Brand Molecule Entry DateGeneric Entry in

Canada

Generic Entry in

the USManufacturer

Table 2: Classification of Sources

Code Source Type Example No of Sources

in the sample

No of Articles in

the source type

1 Professional/Trade magazines Women's Health Weekly 166 5,331

2 Academic medical journals American Journal of Cardiology 31 408

3 Online news sources BusinessWeek Online 38 1,129

4 US TV news CBS News: 60 Minutes 105 677

5 Canada TV news CTV News - PM 2 5

6 US newspapers The Wall Street Journal 230 8,497

7 Canada newspapers The Globe and Mail 17 1,092

8 US regular magazines Time 174 1,281

9 Canada regular magazines Benefits Canada 5 7

10 Everything else The Times 668 14,590

11 News Agencies Agence France-Presse 170 6,247

25

Table 3: Number of Sources and Articles Covering statins by year

All SourcesCanadian

Accessible SourcesAll Sources

Canadian

Accessible Sources

1986 14 3 20 4

1987 36 5 162 30

1988 25 4 61 9

1989 29 4 53 12

1990 41 7 74 11

1991 57 5 111 11

1992 37 6 93 14

1993 83 13 309 43

1994 110 16 435 50

1995 151 21 548 78

1996 167 27 656 87

1997 219 43 1,077 156

1998 264 43 1,566 190

1999 289 49 2,254 229

2000 327 66 2,245 290

2001 518 98 4,688 705

2002 519 112 5,579 837

2003 620 137 7,530 1,558

2004 742 168 9,654 2,688

Total 37,115 7,002

Year

No of ArticlesNo of Sources

26

Table 4: Summary of Publicity Variables

Mean Std. Dev. Min. Max. Mean Std. Dev. Min. Max.

Lipitor 93 320 3.30 5.19 0 22 408 7.91 14.24 0 86

Baycol 41 12 0.29 0.64 0 3 6 0.17 0.50 0 2

Lescol 130 92 0.65 2.09 0 16 90 0.74 1.89 0 11

Mevacor 142 120 0.70 1.15 0 9 114 1.13 2.99 0 21

Pravachol 142 179 1.13 2.06 0 9 251 1.87 5.13 0 32

Crestor 22 81 5.32 5.41 0 19 196 30.41 20.28 4 72

Zocor 142 349 2.35 4.57 0 29 229 2.19 5.10 0 29


Lipitor 93 278 2.98 5.50 0 22 37 0.81 3.11 0 20

Baycol 41 3 0.07 0.35 0 2 0 0 0 0 0

Lescol 130 67 0.51 1.99 0 16 0 0 0 0 0

Mevacor 142 71 0.46 1.08 -1 9 0 0 0 0 0

Pravachol 142 225 1.42 2.12 -1 9 35 0.25 1.26 0 10

Crestor 22 10 0.45 1.53 0 7 0 0 0 0 0

Zocor 142 329 2.15 3.27 0 15 4 0.03 0.24 0 2


Lipitor 93 112 0.73 1.89 -4 7 79 0.88 3.86 -1 28

Baycol 41 3 -0.07 0.26 -1 0 0 0 0 0 0

Lescol 130 44 0.31 1.54 -1 16 4 0.03 0.17 0 1

Mevacor 142 48 0.21 0.67 -1 6 8 0.06 0.52 0 6

Pravachol 142 49 0.27 0.76 -1 5 53 0.56 3.05 0 32

Crestor 22 185 -3.05 7.21 -27 7 40 1.27 3.68 0 14

Zocor 142 96 0.46 1.54 -1 13 44 0.32 2.42 -1 28

Side-Effects# of

Months

Reducing

Risks of Heart

Disease

# of

Months

Non-comparison

Lowering

Cholesterol

Levels

# of

Months

Non-comparison Comparison

Non-comparison Comparison

Comparison

# of

articles

# of

articles

# of

articles

Values

Values

Values Values

Values

Values# of

articles

# of

articles

# of

articles

Table 5: Prescription Size and Advertising Levels

Mean Std Min. Max. Mean Std Min. Max. Mean Std Min. Max.

Lipitor 93 419,611 263,408 3,384 925,001 244,288 85,475 67,851 433,334 33.70 8.89 16 60

Baycol 41 36,873 26,869 846 93,001 159,277 56,812 62,567 377,472 22.37 10.49 0 44

Lescol 130 23,894 7,809 310 37,486 67,263 66,768 0 266,241 7.67 10.62 0 39

Mevacor 142 65,214 23,639 30,014 100,181 23,690 35,985 0 133,456 4.11 7.86 0 27

Pravachol 142 118,210 45,582 35,382 176,228 94,080 71,946 0 285,962 10.27 12.13 0 46

Crestor 22 105,692 66,584 6,293 195,514 288,869 75,345 173,363 481,073 36.91 14.63 2 59

Zocor 142 154,288 87,246 33,084 315,666 81,188 56,974 0 326,300 19.09 12.90 0 48

# of

Months

PRESCRIPTION DETAILING JOURNALAD

27

Table 6: Ontario Drug Benefit Formulary Price32 33 34 35 36 37 38 39

Jul 1992 1993 Dec 1994 May 1996 Dec 1998 Mar 2001 Jan 2003 Sep 2005

Brand Strength Brand

10mg Lipitor 1.60 1.60 1.60 1.60 1.60

20mg Lipitor 2.00 2.00 2.00 2.00 2.00

40mg Lipitor 2.15 2.15 2.15 2.15 2.15

80mg Lipitor 2.15 2.15

0.2mg Baycol 1.20 1.20

0.3mg Baycol 1.45 1.45

0.4mg Baycol 1.60

20mg Lescol 0.75 0.75 0.75 0.75 0.75 0.75

40mg Lescol 1.05 1.05 1.05 1.05 1.05 1.05

80mg Lescol 1.30

20mg Mevacor 1.74 1.73 1.73 1.73 1.73 1.73 1.73 1.73

Generic 1.30 1.30 1.30 1.09 1.09

40mg Mevacor 3.05 3.19 3.19 3.19 3.19 3.19 3.19 3.19

Generic 2.40 2.40 2.40 2.01 2.01

10mg Pravachol 1.53 1.51 1.51 1.51 1.51 1.51 1.51 1.51

Generic 1.06 0.95 0.95

20mg Pravachol 1.80 1.79 1.79 1.79 1.79 1.79 1.79 1.79

Generic 1.25 1.12 1.12

40mg Pravachol 2.15 2.15 2.15 2.15 2.15

Generic 1.51 1.35 1.35

10mg Crestor 1.36

20mg Crestor 1.70

40mg Crestor 1.99

5mg Zocor 1.38 1.41 0.90 0.90 0.90 0.90 0.90 0.90

Generic 0.57

10mg Zocor 1.72 1.78 1.78 1.78 1.78 1.78 1.78 1.78

Generic 1.12

20mg Zocor 2.20 2.20 2.20 2.20 2.20 2.20

Generic 1.39

40mg Zocor 2.70 2.20 2.20 2.20 2.20

Generic 1.39

80mg Zocor 2.20 2.20 2.20

Generic 1.39

Price

Atorvastatin

Cerivastatin

Fluvastatin

Lovastatin

Prvastatin

Rosuvastatin

Simvastatin

Book Number

Effective from

28

Table 7: Landmark Clinical Trials

Title

Publicat-

ion Date

(mm/yy)

JournalDrugs

Studied

No of

Subje-

cts

Follow-

up

Period

Results Sponsors

4S 12/94 Lancet Zocor 4,444 5.4 yrs Reduce risk of coronary events

and improve survival

Merck & Co.

WOSCOPS 11/95 NEJM Pravachol 6,595 4.9 yrs Reduce MI and death from

cardiovascular causes

Bristol-Myers

Squibb

CARE 10/96 NEJM Pravachol 4,159 5 yrs Reduce coronary events Bristol-Myers

Squibb

CURVES** 03/98 Am J

Cardiol

Lipitor*,

Zocor,

Pravachol,

Mevacor,

Lescol

534 8 wks Lower cholesterol levels Parke-Davis

(Warner

Lambert)

AFCAPS/TexCAPS 05/98 JAMA Mevacor 5,705 5.2 yrs Reduce risk for the first acute

coronary event

LIPID 11/98 NEJM Pravachol 9,014 6.1 yrs Reduce the cardiovascular

events and death

Bristol-Myers

Squibb

MIRACL 04/01 JAMA Lipitor 3,086 16 wks Reduce recurrent ischemic

events

Pfizer Inc.

LIPS 06/02 JAMA Lescol 1,677 3.9 yrs Reduce the risk of major

adverse cardiac events

Novartis

HPS 07/02 Lancet Zocor 20,536 5 yrs ReduceMI, stroke, and

revascularisation

Merck & Co.

PROSPER 11/02 Lancet Pravachol 5,804 3.2 yrs Reduce coronary heart disease

death and non-fatal myocardial

infarction risk

Bristol-Myers

Squibb

ALLHAT-LLT 12/02 JAMA Pravachol 10,355 4.8 yrs Reduce neither mortality nor

CHD significantly compared

with ususal care

Pfizer

ASCOT-LLA 05/03 Lancet Lipitor 10,305 3.3 yrs Reduce cardiovascular morbidty Pfizer

ALERT 06/03 Lancet Lescol 2,102 5.1 yrs Reduce Cardiac Death and non-

fatal MI

Novartis

REVERSAL 03/04 JAMA Lipitor*,

Pravachol

2,163 1.5 yrs Reduce coronary atherosclerosis Pfizer Inc.

PROVE IT-TIMI 04/04 NEJM Lipitor*,

Pravachol

4,162 2 yrs Protect against death or

cardiovascular events

Bristol-Myers

Squibb

ALLIANCE 06/04 JACC Lipitor 2,422 4.3 yrs Reduce the risk of first

cardiovascular event

Pfizer

CARDS 08/04 Lancet Lipitor 2,838 3.9 yrs Lower incidence of coronary

events, stroke, and coronary

revascularization procedures

Pfizer

A to Z 09/04 JAMA Zocor 4,498 2 yrs Reduce cardiovascular events. Merck & Co.

* denotes the drug which appears superior than compared statins in the study

** CURVES study is not a landmark study. Lipitor gained an approval from FDA with the study.

29

Table 8: OLS Results

Coef. S.E. Coef. S.E. Coef. S.E. Coef. S.E. Coef. S.E. Coef. S.E.

Detailingjt 0.65* 0.04 0.56* 0.03 0.52* 0.03 0.61* 0.04 0.62* 0.04 0.61* 0.04

JournalADjt -0.05 0.04 0.16* 0.03 0.17* 0.03 -0.02 0.04 -0.01 0.04 0.00 0.04

Publicity_NCjt -0.32 0.21

Publicity_Cjt 0.62* 0.03

Publicity_LC_NCjt -0.02 0.04 0.03 0.04 0.02 0.04 0.00 0.04

Publicity_LC_Cjt 0.49* 0.03 0.15* 0.06

Publicity_RH_NCjt 0.07 0.04 -0.01 0.04 -0.07 0.05 -0.06 0.05

Publicity_SE_NCjt -1.23* 0.46 1.45* 0.57 1.41* 0.57 1.13 0.60

Publicity_SE_Cjt 0.27* 0.07 0.31* 0.06 0.30* 0.06 0.27* 0.06

Publicity_LC_C(Lipitor)jt 0.81* 0.06 0.66* 0.07

Publicity_LC_C(Baycol)jt 1.28* 0.08 1.11* 0.09

Publicity_LC_C(Lescol)jt 0.28* 0.06 0.13 0.08

Publicity_LC_C(Mevacor)jt 0.39* 0.04 0.14 0.08

Publicity_LC_C(Pravachol)jt 0.14* 0.05 0.01 0.06

Publicity_LC_C(Crestor)jt 1.33* 0.16 1.05* 0.18

Publicity_LC_C(Zocor)jt 0.26* 0.05 0.06 0.08

Sum_RHt*LC_Cjt 0.04* 0.01

Sum_RHt*LC_C(Lipitor)jt 0.13* 0.02

Sum_RHt*LC_C(Baycol)jt 0.24* 0.02

Sum_RHt*LC_C(Lescol)jt 0.04* 0.02

Sum_RHt*LC_C(Mevacor)jt 0.04* 0.01

Sum_RHt*LC_C(Pravachol)jt 0.02 0.02

Sum_RHt*LC_C(Crestor)jt 0.15* 0.02

Sum_RHt*LC_C(Zocor)jt 0.03* 0.01

R2

Note * p<0.05.

Variable Definitions

Variable

Detailingjt

JournalADjt

Publicity_NCjt

Publicity_Cjt

Publicity_LC_NCjt

Publicity_LC_Cjt

Publicity_RH_NCjt

Publicity_SE_NCjt

Publicity_SE_Cjt

Sum_RHt*LC_Cjt

Log of monthly stock of number of comparsion articles favoring drug j in the dimension of lowering

cholesterol levels

Log of monthly stock of number of non-comparsion articles favoring drug j in the dimension of reducing

risks of heart disease

Log of monthly stock of number of non-comparsion articles favoring drug j in the dimension of side

effects

Log of monthly stock of number of comparsion articles favoring drug j in the dimension of side effects

Interaction of monthly stock of number of comparison articles favoring drug j in the dimension of

lowering cholesterol levels with sum of monthly stock of articles favoring any statin in the dimension of

reducing risks of heart disease

Log of monthly stock of detailing cost for drug j

Log of monthly stock of medical journal advertising pages for drug j

Log of monthly stock of number of non-comparsion articles favoring drug j

Log of monthly stock of number of comparsion articles favoring drug j

Log of monthly stock of number of non-comparsion articles favoring drug j in the dimension of lowering

cholesterol levels

Definition

(6)

0.9872 0.9935 0.9936 0.9953 0.99500.9952

Variables(1) (2) (3) (5)(4)

30

Table 9: Detailing Estimation Function

Coef. S.E. Coef. S.E. Coef. S.E. Coef. S.E.

Agejt 0.02 0.02 0.00 0.02 -0.00 0.02 0.01 0.05

Agejt*Agejt -0.00 0.00 -0.00 0.00 -0.00 0.00 0.00 0.00

Patent_Expirationjt -7.74* 0.90 -7.84* 0.90 -7.15* 1.00 -7.03* 1.06

Intro_Periodjt 4.99* 1.70 5.07* 1.69 5.28* 1.56 5.19* 1.65

Lipitor 23.94* 0.81 23.41* 0.81 20.09* 0.87 20.39* 1.77

Baycol 15.29* 0.93 15.58* 0.92 15.66* 0.89 16.14* 2.09

Lescol 6.44* 0.83 6.96* 0.83 8.85* 0.94 8.61* 1.20

Mevacor 8.48* 0.98 9.59* 1.03 7.55* 1.41 5.78 3.03

pravachol 12.30* 0.89 12.78* 0.90 12.44* 1.05 11.68* 1.52

Crestor 28.03* 1.22 26.64* 1.59 27.69* 1.56 29.66* 4.43

Zocor 9.28* 0.87 9.64* 0.87 8.10* 1.15 7.79* 1.47

Publicity_LC_NCjt -0.15 0.09 -0.14 0.08 -0.14 0.09

Publicity_LC_Cjt 0.10* 0.03 0.06 0.03 0.06 0.04

Publicity_RH_NCjt 0.22* 0.09 0.16 0.09 0.14 0.10

Publicity_SE_NCjt 0.23 0.12 0.21 0.12 0.20 0.12

Publicity_SE_Cjt -0.09 0.09 -0.12 0.11 -0.03 0.12

R2

Note * p<0.05


Variable

Agejt

Patent_Expirationjt

Intro_Periodjt

Publicity_LC_NCjt Number of non-comparsion articles favoring drug j in the dimension of lowering cholesterol

levels at time t

Variables (1) (2) (3) (4)

0.8538 0.88510.8829

Detailing (x104)

0.8496

Definition

Number of months since the entry of the drug j

Indicator variable representing whether patent for drug j expires at time t

Indicator variable representing whether drug j is within four months of its entry at time t

Publicity_LC_Cjt

Publicity_RH_NCjt

Publicity_SE_NCjt

Publicity_SE_Cjt

levels at time t

Number of comparsion articles favoring drug j in the dimension of lowering cholesterol levels

at time t

Number of non-comparsion articles favoring drug j in the dimension of reducing risks of heart

disease at time t

Number of non-comparsion articles favoring drug j in the dimension of side effects at time t

Number of comparsion articles favoring drug j in the dimension of side effects at time t

31

Table 10: Journal Advertising Estimation Function


Agejt -0.12* 0.03 -0.14* 0.03 -0.27* 0.04 -0.18* 0.07

Agejt*Agejt 0.00 0.00 0.00* 0.00 0.00* 0.00 0.00* 0.00

Patent_Expirationjt -11.64* 1.50 -11.74* 1.50 -6.12* 1.63 -5.89* 1.71

Intro_Periodjt 5.02 2.82 5.19 2.81 2.84 2.55 3.05 2.65

Lipitor 38.45* 1.34 37.52* 1.36 36.70* 1.42 40.96* 2.84

Baycol 24.41* 1.55 24.80* 1.54 27.42* 1.46 32.59* 3.37

Lescol 13.91* 1.37 14.66* 1.39 20.77* 1.54 22.11* 1.92

Mevacor 22.37* 1.63 24.03* 1.71 28.72* 2.29 24.55* 4.88

pravachol 22.44* 1.48 23.21* 1.49 22.24* 1.71 19.78* 2.44

Crestor 37.59* 2.02 34.61* 2.64 37.68* 2.54 44.86* 7.13

Zocor 28.50* 1.45 29.08* 1.45 23.68* 1.88 21.66* 2.36

Publicity_LC_NCjt -0.14 0.14 -0.01 0.13 -0.06 0.14

Publicity_LC_Cjt 0.17* 0.06 0.10 0.06 0.10 0.06

Publicity_RH_NCjt 0.19 0.15 0.02 0.15 -0.00 0.15

Publicity_SE_NCjt 0.39 0.21 0.30 0.19 0.36 0.19

Publicity_SE_Cjt -0.10 0.15 -0.10 0.17 -0.02 0.19

R2

Note * p<0.05


Variable

Agejt

Patent_Expirationjt

Intro_Periodjt

Publicity_LC_NCjt

Publicity_LC_Cjt

Publicity_RH_NCjt

Publicity_SE_NCjt

Publicity_SE_Cjt

Number of non-comparsion articles favoring drug j in the dimension of side effects at time t

Number of comparsion articles favoring drug j in the dimension of side effects at time t

Number of months since the entry of the drug j

Indicator variable representing whether patent for drug j expires at time t

Indicator variable representing whether drug j is within four months of its entry at time t

Number of non-comparsion articles favoring drug j in the dimension of lowering cholesterol

levels at time t

Number of comparsion articles favoring drug j in the dimension of lowering cholesterol levels

at time t

Number of non-comparsion articles favoring drug j in the dimension of reducing risks of heart

disease at time t

Definition

Variables

Journal Advertising

(1) (2) (3) (4)

0.8145 0.8186 0.8608 0.8671

32

Figure 1: Monthly Number of Prescription for Statins

�� ! �"# �! $"%� �� &��%� '�! (�"#�� )� ��

��*��+��, -. /01 2 34 /01 5 -6 /07 5 36 /07 8 9: /07 2 ;. /0< = >; /0< ? >@ /0A 5 3B /0A C >D /0A , -E /0F G DH /0F , -. /0I 2 34 /0I 5 -6 /00 5 36 /00 8 9: /00 2 ;. /JJ = >; /JJ ? >@ /JK 5 3B /JK C >D /JK , -E /JL G DH /JL , -. /J1 2 34 /J1 5 -6 /J7 5 36 /J7 8 9: /J7MN�OPN � � � ��

�� ! �"# �! $"%� �� &��%� '�! (�"#�� )� ��Figure 2: Monthly Market Share of Prescription for Four Leading Statins

�� !"�� #��

�$�%�&��

' () *+, - ./ *+, 0 (1 *+2 0 .1 *+2 3 45 *+2 - 6) *+7 8 96 *+7 : 9; *+< 0 .= *+< > 9? *+< ' (@ *+A B ?C *+A ' () *+D - ./ *+D 0 (1 *++ 0 .1 *++ 3 45 *++ - 6) *EE 8 96 *EE : 9; *EF 0 .= *EF > 9? *EF ' (@ *EG B ?C *EG ' () *E, - ./ *E, 0 (1 *E2 0 .1 *E2 3 45 *E2H �I�JK ��L ��MN �� !"�� #��

33

Figure 3: Monthly Stocks of LC Comparison Publicity

�� ! "��

��#��#��$ %& '() $ *& '() + ,- '() . /0 '(1 2 3/ '(1 4 35 '(6 $ *7 '(6 8 39 '(6 : %; '(< = 9> '(< : %0 '(? . *@ '(? $ %& '(A $ *& '(A + ,- '(A . /0 '(( 2 3/ '(( 4 35 'BB $ *7 'BB 8 39 'BB : %; 'BC = 9> 'BC : %0 'BD . *@ 'BD $ %& 'B) $ *& 'B) + ,- 'B) . /0 'B1 2 3/ 'B1E ��F ��

�� ! "��Figure 4: Monthly Stocks of RH Non-Comparison Publicity

�� !�" #� ��

��$ %& '() $ *& '() + ,- '() . /0 '(1 2 3/ '(1 4 35 '(6 $ *7 '(6 8 39 '(6 : %; '(< = 9> '(< : %0 '(? . *@ '(? $ %& '(A $ *& '(A + ,- '(A . /0 '(( 2 3/ '(( 4 35 'BB $ *7 'BB 8 39 'BB : %; 'BC = 9> 'BC : %0 'BD . *@ 'BD $ %& 'B) $ *& 'B) + ,- 'B) . /0 'B1 2 3/ 'B1E �� F ��

�� !�" #� ��34

Figure 5: Monthly Detailing Stocks for Four Leading Statins

�� !�� "��

#$%��

& '( )*+ , -. )*+ / '0 )*1 / -0 )*1 2 34 )*1 , 5( )*6 7 85 )*6 9 8: )*; / -< )*; = 8> )*; & '? )*@ A >B )*@ & '( )*C , -. )*C / '0 )** / -0 )** 2 34 )** , 5( )DD 7 85 )DD 9 8: )DE / -< )DE = 8> )DE & '? )DF A >B )DF & '( )D+ , -. )D+ / '0 )D1 / -0 )D1 2 34 )D1G HI�J� �� !�� "��

Figure 6: Monthly Medical Journal Advertising Stocks for Four Leading Statins

�� !"�� #��

�$��%��&��' () *+, - ./ *+, 0 (1 *+2 0 .1 *+2 3 45 *+2 - 6) *+7 8 96 *+7 : 9; *+< 0 .= *+< > 9? *+< ' (@ *+A B ?C *+A ' () *+D - ./ *+D 0 (1 *++ 0 .1 *++ 3 45 *++ - 6) *EE 8 96 *EE : 9; *EF 0 .= *EF > 9? *EF ' (@ *EG B ?C *EG ' () *E, - ./ *E, 0 (1 *E2 0 .1 *E2 3 45 *E2H IJ��

�� !"�� #��35

A Data Appendix

In this section of the appendix, we address the following issues: how to select articles and how to

classify articles.

A.1 Selecting the Articles

We collected publicity data from Factiva, a famous information database service provider. Factiva

(http://www.factiva.com) is a division of Dow Jones & Company, that provides the business and

education communities with business and research information. It provides data from more than 14,000

sources (such as newspapers, journals, magazines, news and radio transcripts, etc) from 152 countries

in 22 languages.

We searched articles containing either a brand or a molecule name of statins from the database. As

a result, 37,115 articles in the period of year 1986 to 2004 from Factiva were downloaded. We stored 100

articles together in a RTF (Rich Text Format) file. To convert each article as one record, we converted

RTF files into plain text files and parse the contents of a text file by designing a PHP program, a

website building language. Then, from each record, we extracted a headline, a source, contents and

publication date and organize the data in our own MySQL database. Our web program and MySQL

database provided at least two advantages. First, web-based program and database technology allowed

us to easily make a change in coding scheme or modify the dataset. In this study, we employ a very

large diverse dataset covering almost 20 year and we code them in a very sophisticated way. Therefore,

we have to try a few different coding schemes to figure out the way which results in the least loss of

the information embedded in publicity while coding the publicity. Since we were able to access, search,

read, and modify the records conveniently through any web browser, we were able to code the publicity

and manage the coded results very easily. Figure 7 depicts a screen shot for a list of articles in our

sample. Because the publicity data was stored in a very organized way, we could search and read articles

conveniently. These characteristics of our computer program made a big contribution in finding a proper

36

coding scheme which is necessary to explain the impact of publicity. Second, we could save a large

amount by fully utilizing the benefit of reading through computer screen. For example, we highlighted

important keywords on screen with a different color. We colored statin names in red, clinical study

names in blue and keywords related to efficacies or side effects in green. These highlighted keywords

enabled readers to reduce time on reading articles by skipping the irrelevant parts and focusing on the

sentences with important keywords. This trick seemed to save, at least, more than half of reading time.

Figure 8 depicts a screen shot for one article in our sample.

A.2 Classifying the Articles

Classifying articles was the very challenging part of our study for the following reasons. First, we often

encounter multi-dimensional articles, which can be coded positive in one dimension but negative in

another dimension. For example, one article could report that Crestor lowers cholesterol levels rapidly

but might have a serious side-effects. If we had coded this article in a single dimension, it could

have coded as positive if we emphasized the efficacy in lowering cholesterol levels or negative if we

focus on the side-effects of the drug. Therefore, this single-dimensional coding scheme would lead to

a measurement error. To reduce the possibility of bias caused by a single-dimensional coding scheme,

we interpret each article along multiple dimensions: (i) lowering cholesterol levels (short-term efficacy),

(ii) reducing risks of heart disease (long-term efficacy), and (iii) side effects. For each dimension, we

label an article comparison if the article compares more than two statins, and non-comparison if not.

We also provided detailed instruction on how to code articles. For non-comparison articles, we followed

an approach previous research has adopted. We simply scored each article using a three-step Likert

scale (+1, 0, -1) to assess the negative, neutral, or positive attitude of the article. However, we found

that comparison articles cannot coded. Therefore, we code the data in a more complicated method

than the non-comparison ones in order to avoid losing the information contained in the publicity. We

score a comparison article with sum of two types of its attitudes towards the focal drug: absolute and

37

relative. In an absolute level, we assign each drug “+1”, “0”, or “-1” if the article shows a positive,

neutral, or negative attitude towards the focal drug, respectively, as we do for non-comparison articles.

For a relative level, we assign the focal drug the number of drugs that are reported inferior to the focal

drug in the article. Second, we tried to keep loss of the information as little as possible. Some articles

compare more than two drugs in their contents and if we had coded them simply positive and negative

without considering the existing order among these drugs, we would have lost the information. Third,

reducing personal difference across readers was a big challenge. Since we had to assess a very big body

of publicity, we had to have multiple readers to classify articles and this task opened a high possibility of

getting a big noise. Each reader could have a different impression on each article depending on his/her

perspective. Therefore, we provided a clear instruction on how to code each article. By providing the

detailed instruction, we try to minimized the chance of data collection noise.

B Robustness Check

The correlation between journal advertising and detailing is quite high, which are 0.824 and 0.721 for

their flow and stock variables, respectively. Such high correlations suggest that it could be hard to

separately measure their effects (Berndt et al., 1997). This could explain why the point estimates of

the stock of journal advertising vary across specifications. Since the estimate of the stock of detailing

seems to be more stable. As a robustness check, we have run another set of regressions where we drop

the stock of journal advertising. Most of the results reported in table 12 are robust in this specification

compared to table 8.

We therefore consider an alternative specification where we allow stock of detailing to interact with

the clinical trial dummies. This allow us to capture the idea that the effect of detailing could be

heterogeneous across drugs. We find that our results in publicity are largely robust in table 14

38

Table 11: OLS Results (Brand and Landmark Clinical Study Dummies)


Lipitor -11.98* 0.47 -12.20* 1.11 -6.99* 2.47 -21.99* 3.08 -21.84* 3.06 -20.12* 3.22

Baycol -13.62* 0.48 -12.70* 1.11 -7.07* 2.43 -21.73* 3.08 -21.58* 3.06 -20.18* 3.22

Lescol -13.61* 0.47 -12.79* 1.11 -7.64* 2.47 -21.65* 3.08 -21.47* 3.05 -20.08* 3.22

Mevacor -12.22* 0.46 -11.78* 1.12 -6.66* 2.47 -20.67* 3.08 -20.25* 3.06 -18.84* 3.22

pravachol -13.19* 0.48 -12.27* 1.12 -7.14* 2.48 -20.97* 3.10 -20.77* 3.07 -19.45* 3.24

Crestor -12.51* 0.49 -14.14* 1.12 -8.83* 2.44 -27.40* 3.66 -26.80* 3.64 -23.85* 3.65

Zocor -12.86* 0.45 -12.61* 1.10 -7.54* 2.47 -21.07* 3.06 -20.52* 3.04 -19.32* 3.22

4S 0.91* 0.10 0.65* 0.10 0.57* 0.11 0.67* 0.11 0.53* 0.12 0.54* 0.13

WOSCOPS 0.55* 0.15 0.13 0.12 -0.06 0.13 0.33* 0.12 0.37* 0.12 0.35* 0.12

CARE 0.34* 0.15 0.37* 0.11 0.41* 0.11 0.38* 0.10 0.38* 0.09 0.37* 0.10

AFCAPS

/TexCAPS

1.08* 0.11 0.96* 0.08 0.81* 0.10 0.67* 0.08 0.74* 0.09 0.75* 0.09

LIPID 0.32* 0.11 0.09 0.08 0.13 0.08 0.15 0.08 0.13 0.08 0.13 0.08

MIRACL 1.00* 0.11 0.14 0.09 0.12 0.09 -0.35* 0.10 -0.38* 0.10 -0.43* 0.12

LIPS 0.64* 0.14 0.45* 0.10 0.35* 0.11 0.32* 0.11 0.37* 0.11 0.38* 0.11

HPS 1.08* 0.10 0.19* 0.09 0.15 0.08 0.37* 0.11 0.35* 0.10 0.28* 0.11

PROSPER 0.74 0.43 0.46 0.31 0.39 0.30 0.47 0.27 0.43 0.27 0.45 0.27

ALLHAT-LLT 0.22 0.44 -0.06 0.31 -0.09 0.31 0.11 0.27 0.04 0.27 0.04 0.28

ASCOT-LLA 0.30 0.16 -0.12 0.14 -0.16 0.12 -0.55* 0.11 -0.61* 0.12 -0.81* 0.14

ALERT 0.34* 0.16 -0.25* 0.12 -0.24* 0.12 -0.12 0.13 -0.17 0.13 -0.17 0.15

REVERSAL_

Lipitor

0.04 0.44 -0.33 0.32 -0.36 0.31 -0.61* 0.28 -0.65* 0.27 -0.73* 0.29

REVERSAL_

Pravachol

0.21 0.44 -0.53 0.31 -0.56 0.31 -0.23 0.28 -0.36 0.28 -0.34 0.29

PROVE IT_

Lipitor

0.08 0.49 0.03 0.35 0.01 0.35 0.06 0.30 0.05 0.30 0.04 0.31

PROVE IT_

Pravachol

0.15 0.45 0.10 0.32 0.11 0.32 0.09 0.28 0.06 0.27 0.08 0.28

ALLIANCE -0.01 0.49 0.01 0.35 0.02 0.35 -0.07 0.30 -0.07 0.30 -0.09 0.31

CARDS 0.09 0.47 0.04 0.34 -0.14 0.34 -0.29 0.30 -0.30 0.29 -0.36 0.30

A to Z 0.57* 0.26 0.25 0.19 0.21 0.19 0.07 0.17 0.03 0.17 0.04 0.18

(6)

Note * p<0.05

PROVE IT and REVERSAL are studies which compare efficacies of Lipitor and Pravachol.

Variables(1) (2) (3) (4) (5)

39

Table 12: OLS Results without Journal Advertising


Detailingjt 0.60* 0.03 0.68* 0.02 0.64* 0.02 0.59* 0.02 0.61* 0.03 0.62* 0.02



Publicity_LC_NCjt 0.02 0.04 0.03 0.04 0.02 0.04 0.00 0.04

Publicity_LC_Cjt 0.46* 0.03 0.15* 0.06

Publicity_RH_NCjt 0.00 0.04 -0.00 0.04 -0.07 0.04 -0.06 0.04

Publicity_SE_NCjt -1.63* 0.46 1.39* 0.56 1.38* 0.56 1.15 0.59

Publicity_SE_Cjt 0.24* 0.07 0.32* 0.06 0.30* 0.06 0.27* 0.06



Publicity_LC_C(Lescol)jt 0.31* 0.05 0.14* 0.07

Publicity_LC_C(Mevacor)jt 0.39* 0.04 0.14 0.08

Publicity_LC_C(Pravachol)jt 0.14* 0.05 0.01 0.06


Publicity_LC_C(Zocor)jt 0.26* 0.05 0.06 0.08





Sum_RHt*LC_C(Mevacor)jt 0.04* 0.01

Sum_RHt*LC_C(Pravachol)jt 0.02 0.02


Sum_RHt*LC_C(Zocor)jt 0.03* 0.01

R2

Note * p<0.05.


Variable

Detailingjt

Publicity_NCjt

Publicity_Cjt

Publicity_LC_NCjt

Publicity_LC_Cjt

Publicity_RH_NCjt

Publicity_SE_NCjt

Publicity_SE_Cjt

Sum_RHt*LC_Cjt

Log of monthly stock of number of comparsion articles favoring drug j in the dimension of

lowering cholesterol levels

Log of monthly stock of number of non-comparsion articles favoring drug j in the dimension

of reducing risks of heart disease


of side effects


side effects

Interaction of monthly stock of number of comparison articles favoring drug j in the

dimension of lowering cholesterol levels with sum of monthly stock of articles favoring any

statin in the dimension of reducing risks of heart disease

Definition





of lowering cholesterol levels

(6)

0.9872 0.9932 0.9934 0.9952 0.9953 0.9950

(5)Variables

(1) (2) (3) (4)

40

Table 13: OLS Results without Journal Advertising (Brand and Landmark Clinical Study Dummies)


Lipitor -11.68* 0.42 -12.74* 1.13 -5.56* 2.51 -21.58* 2.99 -21.62* 2.97 -20.20* 3.13

Baycol -13.31* 0.41 -13.35* 1.12 -5.80* 2.47 -21.30* 2.98 -21.35* 2.96 -20.26* 3.12

Lescol -13.29* 0.40 -13.46* 1.13 -6.34* 2.51 -21.26* 3.00 -21.26* 2.97 -20.16* 3.14

Mevacor -11.92* 0.40 -12.35* 1.14 -5.29* 2.51 -20.27* 3.00 -20.03* 2.98 -18.93* 3.14

pravachol -12.86* 0.42 -12.96* 1.13 -5.80* 2.51 -20.56* 3.01 -20.55* 2.98 -19.53* 3.14

Crestor -12.19* 0.43 -14.68* 1.13 -7.50* 2.47 -26.79* 3.50 -26.47* 3.48 -23.97* 3.51

Zocor -12.58* 0.40 -13.15* 1.11 -6.03* 2.50 -20.68* 2.97 -20.30* 2.95 -19.40* 3.14

4S 0.90* 0.10 0.69* 0.10 0.62* 0.11 0.66* 0.11 0.52* 0.11 0.55* 0.13

WOSCOPS 0.52* 0.15 0.24 0.12 0.07 0.13 0.31* 0.11 0.36* 0.11 0.36* 0.12

CARE 0.34* 0.15 0.38* 0.11 0.43* 0.11 0.38* 0.10 0.37* 0.09 0.37* 0.10

AFCAPS 1.13* 0.11 0.84* 0.08 0.75* 0.10 0.67* 0.08 0.74* 0.09 0.75* 0.09

LIPID 0.38* 0.10 -0.04 0.08 0.00 0.08 0.17* 0.07 0.14 0.07 0.13 0.07

MIRACL 1.01* 0.11 0.19* 0.09 0.22* 0.09 -0.35* 0.10 -0.38* 0.10 -0.43* 0.12

LIPS 0.71* 0.13 0.27* 0.10 0.19 0.11 0.31* 0.11 0.37* 0.11 0.38* 0.11

HPS 1.10* 0.09 0.20* 0.09 0.18* 0.08 0.37* 0.11 0.35* 0.10 0.28* 0.11

PROSPER 0.77 0.43 0.40 0.31 0.33 0.31 0.47 0.27 0.43 0.26 0.45 0.27

ALLHAT-LLT 0.22 0.44 -0.06 0.32 -0.09 0.32 0.11 0.27 0.04 0.27 0.04 0.28

ASCOT-LLA 0.29 0.16 -0.07 0.14 -0.06 0.12 -0.55* 0.11 -0.61* 0.11 -0.81* 0.14

ALERT 0.34* 0.16 -0.23 0.12 -0.20 0.12 -0.15 0.12 -0.19 0.12 -0.16 0.13

REVERSAL_

Lipitor

0.04 0.44 -0.30 0.33 -0.31 0.32 -0.61* 0.28 -0.65* 0.27 -0.73* 0.29

REVERSAL_

Pravachol

0.22 0.44 -0.51 0.32 -0.52 0.32 -0.24 0.28 -0.36 0.28 -0.34 0.29

PROVE IT_

Lipitor

0.08 0.49 0.03 0.35 0.01 0.35 0.05 0.30 0.05 0.30 0.04 0.31

PROVE IT_

Pravachol

0.15 0.45 0.10 0.33 0.11 0.32 0.10 0.28 0.06 0.27 0.08 0.28

ALLIANCE -0.01 0.49 0.01 0.36 0.04 0.35 -0.07 0.30 -0.07 0.30 -0.09 0.31

CARDS 0.09 0.47 0.04 0.34 -0.09 0.35 -0.29 0.30 -0.31 0.29 -0.35 0.30

A to Z 0.58* 0.26 0.26 0.20 0.23 0.19 0.08 0.17 0.03 0.17 0.04 0.18

(6)

Note * p<0.05


Variables(1) (2) (3) (4) (5)

41

Table 14: OLS Results including detailing interacted with clinical results


Detailingjt 1.12* 0.03 0.99* 0.03 0.96* 0.03 0.87* 0.03 0.87* 0.03 0.89* 0.03



Publicity_LC_NCjt -0.00 0.04 0.03 0.03 0.02 0.03 0.02 0.03

Publicity_LC_Cjt 0.22* 0.03 -0.04 0.06

Publicity_RH_NCjt 0.14* 0.04 0.09* 0.03 0.05 0.04 0.07 0.04

Publicity_SE_NCjt -2.68* 0.42 0.46 0.46 0.49 0.46 0.46 0.48

Publicity_SE_Cjt -0.14 0.08 -0.12 0.06 -0.11 0.06 -0.16* 0.07



Publicity_LC_C(Lescol)jt 0.09 0.04 0.00 0.06

Publicity_LC_C(Mevacor)jt 0.01 0.04 -0.13 0.08

Publicity_LC_C(Pravachol -0.06 0.05 -0.10 0.06


Publicity_LC_C(Zocor)jt 0.03 0.06 -0.06 0.08





Sum_RHt*LC_C(Mevacor) 0.01 0.01

Sum_RHt*LC_C(Pravacho 0.00 0.02


Sum_RHt*LC_C(Zocor)jt 0.02 0.01

R2

Note * p<0.05.


Variable

Detailingjt

Publicity_NCjt

Publicity_Cjt

Publicity_LC_NCjt

Publicity_LC_Cjt

Publicity_RH_NCjt

Publicity_SE_NCjt

Publicity_SE_Cjt

Sum_RHt*LC_Cjt


of reducing risks of heart disease


of side effects


side effects

Interaction of monthly stock of number of comparison articles favoring drug j in the

dimension of lowering cholesterol levels with sum of monthly stock of articles favoring any

statin in the dimension of reducing risks of heart disease

Definition





of lowering cholesterol levels


lowering cholesterol levels

(6)

0.9939 0.9947 0.9952 0.9970 0.9970 0.9969

(5)Variables

(1) (2) (3) (4)

42

Table 15: OLS Results including detailing interacted with clinical results (Brand and Landmark ClinicalStudy Dummies)


D*4S 0.71* 0.19 0.17 0.19 0.60* 0.20 1.02* 0.19 0.95* 0.19 0.99* 0.20

D*WOSCOPS 1.91 3.97 2.95 3.71 3.05 3.56 1.92 2.81 2.11 2.81 1.96 2.88

D*CARE -2.30 4.00 -3.66 3.74 -3.50 3.59 -1.81 2.84 -2.13 2.84 -1.93 2.92

D*AFCAPS -1.00* 0.04 -0.66* 0.05 -0.76* 0.05 -0.77* 0.04 -0.77* 0.04 -0.77* 0.04

D*LIPID -1.08* 0.54 -0.19 0.52 -0.70 0.50 -1.44* 0.43 -1.23* 0.44 -1.40* 0.45

D*MIRACL 1.50 0.87 0.96 0.84 1.72* 0.83 0.37 0.67 0.25 0.67 -0.41 0.71

D*LIPS -1.00* 0.48 -0.49 0.45 -0.40 0.43 -0.57 0.35 -0.56 0.35 -0.52 0.36

D*HPS -2.05* 0.25 -1.02* 0.26 -1.49* 0.25 -1.91* 0.28 -1.80* 0.28 -1.85* 0.32

D*PROSPER -0.00 0.02 0.01 0.02 -0.00 0.02 -0.01 0.02 0.39 0.28 0.28 0.30

D*ALLHAT-LLT 0.31 0.38 0.60 0.35 0.75* 0.34 0.23 0.28 -0.00 0.02 -0.01 0.02

D*ASCOT-LLA -2.27 3.52 -4.29 3.32 -4.81 3.17 -7.51* 2.51 -7.80* 2.51 -9.66* 2.61

D*ALERT 0.06 0.55 0.16 0.51 -0.09 0.49 0.09 0.39 0.14 0.39 0.16 0.40

D*REVERSAL_L 0.00 0.02 0.00 0.02 0.01 0.02 0.01 0.02 0.01 0.02 10.94 24.89

D*REVERSAL_P -0.05 0.98 -0.72 0.91 -1.06 0.88 -0.01 0.71 -0.14 0.71 0.00 0.02

D*PROVE IT_L -3.45 34.38 -1.19 32.12 1.11 30.81 4.98 24.30 5.48 24.24 0.00 0.02

D*PROVE IT_P -0.00 0.03 -0.00 0.03 -0.01 0.03 -0.00 0.02 -0.00 0.02 -0.08 0.74

D*ALLIANCE -2.52 43.55 -0.00 0.03 -14.17 39.18 -9.12 30.91 -8.68 30.83 -0.01 0.02

D*CARDS 0.01 0.03 -2.05 40.64 0.03 0.03 0.02 0.02 0.01 0.02 -10.62 31.62

D*A to Z -0.82 4.06 -0.63 3.79 -3.13 3.79 -2.30 3.00 -2.20 2.99 -2.76 3.07

Lipitor -19.23* 0.42 -17.38* 1.31 -3.71 2.34 -19.72* 2.47 -19.87* 2.46 -19.73* 2.57

Baycol -20.77* 0.41 -18.39* 1.32 -5.03* 2.28 -20.51* 2.44 -20.63* 2.44 -20.69* 2.55

Lescol -20.57* 0.40 -18.33* 1.32 -4.69* 2.34 -19.49* 2.47 -19.62* 2.46 -19.68* 2.57

Mevacor -19.20* 0.40 -17.10* 1.34 -3.48 2.34 -18.10* 2.47 -18.07* 2.46 -18.23* 2.57

pravachol -20.37* 0.42 -18.01* 1.33 -4.45 2.34 -19.04* 2.47 -19.18* 2.47 -19.24* 2.58

Crestor -19.81* 0.42 -18.71* 1.31 -4.99* 2.31 -24.72* 2.86 -24.67* 2.85 -23.17* 2.86

Zocor -19.72* 0.40 -17.74* 1.30 -4.14 2.33 -18.64* 2.44 -18.59* 2.44 -18.68* 2.57

4S -9.55* 2.71 -1.85 2.71 -8.20* 2.83 -14.15* 2.72 -13.28* 2.74 -13.87* 2.85

WOSCOPS -27.53 58.19 -42.88 54.33 -44.66 52.18 -27.88 41.21 -30.64 41.13 -28.44 42.21

CARE 34.08 58.72 54.04 54.87 51.78 52.68 26.86 41.72 31.59 41.68 28.65 42.75

AFCAPS 13.93* 0.52 9.39* 0.67 10.40* 0.65 10.45* 0.58 10.49* 0.57 10.59* 0.57

LIPID 16.14* 8.05 2.92 7.72 10.58 7.47 21.53* 6.32 18.49* 6.48 20.96* 6.65

MIRACL -22.52 13.41 -14.50 12.85 -26.00* 12.78 -5.88 10.27 -4.08 10.28 5.98 10.88

LIPS 14.39* 6.38 7.27 6.00 5.93 5.76 8.29 4.63 8.07 4.62 7.51 4.79

HPS 30.46* 3.59 15.18* 3.77 22.20* 3.67 28.45* 4.04 26.74* 4.12 27.56* 4.62

PROSPER -5.39 3.86 -3.91 4.20

ALLHAT-LLT -4.27 5.06 -8.17 4.74 -10.33* 4.60 -3.31 3.73

ASCOT-LLA 35.21 54.53 66.37 51.43 74.34 49.09 116.00* 38.95 120.36* 38.91 148.92* 40.42

ALERT -0.78 7.15 -2.11 6.68 1.16 6.43 -1.14 5.08 -1.80 5.07 -2.04 5.22

REVERSAL_L -170.15 387.35

REVERSAL_P 0.65 12.74 9.21 11.92 13.89 11.53 0.16 9.24 1.82 9.25

PROVE IT_L 53.80 535.03 18.53 499.90 -17.25 479.51 -77.53 378.25 -85.17 377.34

PROVE IT_P 1.06 9.52

ALLIANCE 39.09 677.68 220.48 609.65 141.72 480.92 134.97 479.75

CARDS 32.08 632.43 165.40 492.11

A to Z 11.07 55.04 8.57 51.39 42.71 51.41 31.20 40.66 29.84 40.57 37.50 41.62

D*, _L and _P stand for Detailing*, _Lipitor and _Pravachol, respectively.

(6)

Note * p<0.05


Variables(1) (2) (3) (4) (5)

43

Table 16: Detailing/Journal Advertising Estimation Function (Clinical Study Dummies)


4S 2.44 1.36 2.99 1.58 16.88* 2.22 18.46* 2.55

WOSCOPS 0.01 1.75 -1.02 1.94 17.45* 2.86 16.16* 3.12

CARE 4.38* 1.80 4.11* 1.90 -2.05 2.94 -1.94 3.05

AFCAPS/TexCAPS 1.32 1.45 0.09 1.84 -1.21 2.36 -4.59 2.96

LIPID -4.38* 1.35 -4.29* 1.68 -12.41* 2.21 -9.89* 2.71

MIRACL 7.94* 1.33 6.99* 1.65 11.16* 2.17 8.29* 2.66

LIPS -8.13* 1.63 -8.38* 2.79 -1.39 2.66 -4.35 4.48

HPS -3.22* 1.40 -2.75 2.83 -13.88* 2.28 -10.74* 4.55

PROSPER -5.58 5.05 -5.45 5.22 0.91 8.23 -1.83 8.39

ALLHAT-LLT 1.02 5.14 1.54 5.24 0.83 8.38 -0.62 8.42

ASCOT-LLA 1.15 2.05 -0.13 3.02 7.38* 3.34 7.65 4.86

ALERT -0.20 1.86 -0.32 2.85 0.60 3.03 0.46 4.58

REVERSAL_Lipitor 1.32 5.26 0.96 5.30 2.77 8.58 2.70 8.52

REVERSAL_Pravachol 0.87 5.34 0.03 5.41 -0.78 8.70 -2.63 8.70

PROVE IT_Lipitor -3.79 5.78 -3.57 5.82 -7.44 9.43 -7.68 9.35

PROVE IT_Pravachol 0.07 5.36 1.76 5.53 1.42 8.73 3.62 8.89

ALLIANCE -1.60 5.80 -1.40 5.83 -2.64 9.46 -2.30 9.38

CARDS 3.09 5.87 4.13 6.05 1.33 9.57 1.93 9.72

A to Z 2.28 3.25 -0.01 4.02 0.94 5.29 0.53 6.46

Other_4S 0.98 1.18 -0.60 1.90

Other_WOSCOPS -1.30 1.25 -6.57* 2.01

Other_CARE -0.28 1.19 2.59 1.91

Other_AFCAPS/TexCAPS -0.50 1.13 -5.50* 1.81

Other_LIPID 0.55 1.13 3.55 1.82

Other_MIRACL -1.76 1.10 -1.38 1.77

Other_LIPS -0.32 2.36 -1.55 3.80

Other_HPS 0.76 2.51 4.51 4.04

Other_PROSPER -4.29 2.74 -3.83 4.40

Other_ALLHAT-LLT 5.39* 2.69 0.33 4.33

Other_ASCOT-LLA -1.17 2.35 1.52 3.77

Other_ALERT -0.26 2.25 -0.08 3.62

Other_REVERSAL 0.32 2.66 5.09 4.28

Other_PROVE IT 0.43 2.84 -6.17 4.57

Other_ALLIANCE -1.22 2.61 -4.95 4.20

Other_CARDS 1.71 3.14 5.35 5.05

Other_A to Z -3.43 2.61 -3.12 4.20

Other_ indicates that the drug is not tested in the clinical trial.

Note * p<0.05


Variables

Detailing (x104) Journald Advertising

(3) (4) (3) (4)

44

Fig

ure

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45

Fig

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the effects of publicity on demand: the case of anti-cholesterol drugs

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