0022- i 821
`No. 3
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`THE JOURNAL OF INDUSTRIAL ECONOMICS
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`Volume XLVIII
`September 2000
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`THE IMPORTANCE OF DOCTORS’ AND PATIENTS’
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`PREFERENCES IN THE PRESCRIPTION DECISION*
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`ANDREA CoscELLi’r
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`This paper studies the contribution of doctor and patient ‘habit’ to
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`persistence in market shares in prescription drug markets. My unique
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`panel dataset allows me to estimate the probability of switching brands
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`as a function of patient and doctor attributes, with an emphasis on
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`past prescribing behaviour so as to capture the degree of persistence. I
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`find significant evidence of time-dependence in prescription choices
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`for both doctors and patients, which seems to imply that in molecular
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`subrnarkets in which brands are not allowed to compete on the basis of
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`price, doctor and patient ‘habit’ at the micro-level can translate into
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`sticky and persistent market shares at the aggregate level.
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`I.
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`INTRODUCTION
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`EN stirs PAPER, I study the contribution of doctor and patient ‘habit’ to
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`persistence in market shares among therapeutically equivalent prescription
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`drugs. While, similar issues have arisen in the recent literature about the
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`competition between generic and branded drugs,
`they are especially
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`puzzling in the Italian pharmaceutical market.
`in Italy, regulatory fiat
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`imposes uniform prices across ail vendors of drugs which utilize the same
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`active ingredient, thus eliminating price variation as an irnportant avenue
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`of differentiation among otherwise therapeutically equivalent drugs, which
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`is true in drug markets with generic competitors. My unique panel dataset
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`allows me to estimate the probability of switching brands as a function
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`of patient and doctor attributes, with an emphasis on past prescribing
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`behaviour so as to capture the degree of persistence.
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`This analysis can shed light on several aspects of market structure in
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`the pharmaceutical industry. First, there is a growing body of literature
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`from the European Commission through a TMR
`*1 acknowledge financial support
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`fellowship #ERBFMBIC'i‘972232. I would like to thank the Istituto Superiore dz‘ Sanita’ for
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`the use of their data. This paper was previously circulated under the titie ‘Are Market Shares
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`in Drug Markets Affected by Doctors’ and Patients‘ E-“rel"erences for Brands?'. Seminar
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`participants at Stanford University (G813 and Department of Economics), Royal Holloway
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`and UCL have provided valuable comments.
`I would like to especially thank my principal
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`adviser, Peter Reiss, who constantly helped me improve this paper, and the editor David
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`Genesove for his useful comments. Mike Mazzeo, Fiona Scott Morton, Andrea Shepard,
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`Matthew Siium and an anonymous referee have also provided many uscfui suggestions. All
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`errors, however, are my own.
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`1' Author’s afliliation: National Economic Research Associates, 15 Stratford Place, London
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`WIN 9AF, UK.
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`emcz:'I.'- Andrea. CosceIIi@riera. com
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`—'D Blackwell Publishers Ltd. 2006. 108 Cowley Road, Oxford OX4 lJ¥"'. UK, and 350 Main Street, Malecn. MA (32148. USA.
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`ANDREA COSCELLI
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`that tries to explain observed market segmentation using data on national
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`market shares. Empirical observations of market shares for trade—name
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`and generic drugs in p0st—pateI1t therapeutic categories in the US market
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`usually indicate a degree of segmentation between branded drugs and their
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`generic equivalents, arising from a finite cross~p2‘ice elasticity between the
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`two types of drugs (the cross-price elasticity between two homogeneous
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`goods should be infinite). However,
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`heterogeneity. The micro dataset at hand allows me both (i) to controi for
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`individual heterogeneity and (ii) to explore the degree of time—dependence
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`in drug choices, both of which can be important
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`substantial and persistent differences in market shares among therapeuti—
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`cally equivalent drugs.
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`Second, in recent years, we have witnessed a surge in direct advertising
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`to consumers by pharmaceutical companies for prescription drugs sold in
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`the US market. The amount spent on direct—to—consumer prescription drug
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`advertising rose from US$3S1n in 1987 to US$357m in 1995, US$610rn in
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`1996, and over US$1 billion in 1997 (NERA [l999]). This spending choice
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`refiects a widespread belief within the pharmaceuticai
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`patients should have a role in the choice of prescription drugs, This paper
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`directly studies the patient’s roie in pharmaceutical choice.
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`Firialiy, the most important institutionai features of the Italian market
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`during the sample period Such as the important role of licensed products,
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`limited patient c0—payments, and Iack of direct financial
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`doctors to prescribe cheaper drugs characterize aimost every EU country
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`(NERA {I999}).
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`I use a new panel dataset provided by the Italian National Health
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`Institute, which inciudes all the prescriptions in the anti—ulcer market from
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`i990ml992 for a 10% random sample of the population of Rome aged
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`15-85. This dataset allows researchers a glimpse into the dynamics of
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`prescription behavior at the micro ievel which is not possibie with the
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`predominantly aggregate and! or cross~sectionai datasets which have been
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`used in most studies of pharmaceutical markets to date.
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`My main conclusions are as follows. I begin by testing the null hypo-
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`thesis of whether doctors and/or patients are indifferent between dntfierent
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`brands of the same molecule, as we would expect given their therapeutic
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`equivaience. After reiecting the hypothesis, I attempt to isoiate both the
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`patienblevel and the doctor—level factors which are responsible for product
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`differentiation.
`I focus specificaliy on the degree of time-dependence in
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`doctors’ and patients’ drug choices by testing whether the patients show
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`state dependence in their purchasing patterns, and whether the doctors
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`exhibit habit persistence. I find significant evidence of doctor and patient
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`‘habit’, which imply that in molecular sub markets in which brands are not
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`ailowed to compete on the basis of price, habit persistence at the micro-
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`PREFERENCES IN THE PRESCRIPTION DECISION
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`level can translate into sticky and persistent market shares at the aggregate
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`The paper is organized as follows. In the next section I survey the
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`previous empirical literature. In Section ill, I describe the dataset used in
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`the estimation. Section IV describes my empirical specification, while
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`Section V reviews the results. A summary of the results make up the final
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`II. DOCTORS’ DEMAND
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`While the present study focuses on doctors’ demand for pharmaceutical
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`products, most of the recent literature on pharmaceuticals (for example,
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`Caves et al. [I991], Caves and I-iurwitz [I988], Berndt at :21. {E997}, Scott
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`Morton {i997, 2000], and Scherer [l993]) has focused on supp1y~side issues
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`(e.g., entry, pricing, advertising, R&D races). In his comment on Caves
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`er al.
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`following doctors’
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`prescriptions over time wouid be the only way to understand the major
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`determinants of the demand for pharmaceuticals. The panel data 1 use
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`allow me to separately identify doctor and patient efibcts.
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`Much of the previous work on the demand for pharmaceuticals has used
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`aggregate, rnarket—share data, which are much better suited to measuring
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`the degree of differentiation between various drugs rather than explain its
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`causes. For example, Stern H995]
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`branded and generic drugs, while Eliison er a1. {1997] find a high elasticity
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`of substitution between generic and branded drugs.
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`One recent rnicrociata»based anaiysis of the demand for pharmaceuticals
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`is that by Hellerstein [I998]. She focuses on doctors’ choices between
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`branded and generic versions of drugs for which a patent has recently
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`expired. Significantly, she finds some evidence of habit persistence in the
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`prescription behavior of physicians, even after controliing for observable
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`characteristics of physicians and patients. Unfortunately, her dataset does
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`not allow her to test for patients’ effects owing to data limitations, While
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`her dataset allows for an analysis of financial incentives due to third—party
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`payer variation. My dataset, on the other hand, has multiple observations
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`for doctor-«patient interactions, prescription of the same molecule by a
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`single doctor to many patients, and prescriptions of the same drug by
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`many doctors.
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`Gorecici
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`and licensees in Canada: an institutional setting very similar to the
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`Italian market. He only observes aggregate data, but he is able to take
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`advantage of the regulatory variation among Canadian provinces to
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`competitive
`empirical
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`conciusions in [E9863 are consistent with my results: ‘. .. Since physicians
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`still Write, by and large, brand name prescriptions for the pioneering
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`ANDREA COSCELLE
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`brand, unless an element of price competition is introduced at the level of
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`the pharmacist the pioneering brand will continue to dominate the market
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`[. . .]. Hence it
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`differences between the pioneering and late
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`introduction of price competition that results in the late entrants capturing
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`Pharmaceutical markets are subdivided into therapeutic classes. Follow-
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`ing most of the recent economic literature on pharmaceuticals (e.g., Stern
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`[l99S}), I regard a therapeutic class as having severai sub-markets. I define
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`a therapeutic market as a 4—digit ATC code (for example, A028 contains
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`all the anti-ulcer drugs), and a sub~market as a specified molecule (for
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`exampie,
`rantzidine). The ATC code is an international classification
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`scheme which ciassifies drugs by target part of the anatomy, mechanism of
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`action, and chemical and therapeutic characteristics. This is a natural
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`definition of demand because a 4»digit ATC code inciudes all the molecules
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`which can theoretically be prescribed for a certain diagnosis. The
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`molecules themselves differ according to side effects,
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`other drugs, specific indications and prices. In the markets I study, a
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`physician typically decides the appropriate molecule for the diagnosis and
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`then she decides which trade-name’ version of the molecule to prescribe to
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`My work focuses on a particular therapeutic market: anti-ulcer drugs
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`(A0213).
`I analyze this market because it accounts for a considerable
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`proportion of world—wide expenditure on pharmaceuticals (around 5%,
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`IMS International [I996}). Ulcers also required repeated treatment in the
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`early 1990s} a key feature of my analysis.
`I analyze six molecule
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`submarkets (famotidine, ranitidine, nizattdine, roxaridine, omeprazole and
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`misaprostole), which represent more than 90% of the prescriptions during
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`the sample period (1990-4992). I restrict niy sample to these six molecules
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`because the other molecules represent more ‘mature’ and smalier sub-
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`markets, where some of the prices for identical brands dif‘fer.3 In each sub-
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`market
`there is a patenbholder and licensees marketing the rnoiecuie.
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`with the dimensions {among others,
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`recognition) according to which these products ‘differ’. In my analysis I
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`focus on competing drugs based on the same active ingredient and
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`‘All the drugs sold under a license or a patent in the Italian market have a trade name.
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`alt has recently been found that app-roxirnateiy 80% of peptic ulcers can be cured by
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`eradicating Hei'i'cobap.rer Pylon‘, a bacterium responsible for the recurrence of ulcers, by using
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`a combination of antibiotics and anti-nicer drugs (Graham [i993]).
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`3 Producers of older molecules had their prices equalized only upon applying for a price
`revision, which happened much later. Moreover, some of the producers in these excluded sub-
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`marlcets are very small firms for whom the assumption ofidenticai quaiity might not hold.
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`@ Blackwell Publishers Ltd. 2900.
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`PREFERENCES IN THE piusscstrrtom DlZCtSi0N
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`marketed by important producers entering the market at
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`time.4
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`the same
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`III.
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`THE DATA
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`The main dataset (provided by the Istituto Superiore della Sandra ’) records,
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`for a 10% sample of the population of the Metropolitan Area of Rome
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`aged 15-85, all
`the prescriptions in the anti—ulcer (A0213) drug market
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`during the period 1990——}992. The sample is stratified according to age and
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`gender; so that the results are representative of the Rome population.
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`This patient-level dataset contains over 310,000 observations. An obser-
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`vation records the identity of the prescribing doctor, the identity of the
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`patient, the year and month, and the particular presentation form of the
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`drug prescribed (for example,
`i package of ZANTAC 20 tablets, 350 mg
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`each). An observation indicates exactly the drug bought by the patient,
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`because the records are collected from pharmacies. In the patient-level
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`dataset there are more than 3,400 doctors prescribing at least once to one
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`of the in-sample patients. A supplementary dataset from the same source
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`records all the prescriptions that 350 of these doctors wrote for any of
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`their patients during the same period. The supplementary doctor~based
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`dataset contains over 710,000 prescriptions and each observation records
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`exactly the same information as the patienbievel ciataset. The finai dataset
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`used in my estimations has more than 75,000 observations; it retains all
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`the observations in the patienblevel ciataset for the patients who received
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`least one prescription from one of the 350 doctors whose entire
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`prescription history is known.
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`Italian Marker Three important characteristics of the Itaiian pharmaw
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`ceutical industry are: (i) there is no price and third-party payer variation,
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`(ii) the 0V€I'-§he'C0iIni.{5!‘
`(OTC) market was tiny in the period of interest,
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`and direct advertising to patients for prescription drugs had not yet
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`started} and (iii) during the sample period, the pharmacist had no power
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`to subsgtitute generics for trade-name drugs, as he does in many American
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`Doctors’ Prescribing Behavior Doctors heavily prescribe across brands:
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`‘By doing this I believe I have effectively controlled for alt ‘objective’ dimensions of
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`difierentiation between drugs; therefore I can proceed with my tests of doctor or patient
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`indifference fairly confident
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`I have controlied for a large share of drug-specific
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`heterogeneity.
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`5 It is currently iliegal throughout the EU and wit} remain so for several years even though
`the subfieet is now occasionaliy raised.
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`5Hellersteir2‘s clataset, therefore, potentially contains a iarge amount of measurement error
`in the prescription variable for states where substitution with generics is mandatory.
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`tit Blackweil Publishers Ltd. 2000.
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`354
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`ANDREA coscsLLt
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`for example, 98.6% of the in-sarnpie doctors prescribed each of the three
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`available brands of omeprazole at ieast once during the sample period.
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`Moreover, doctors usually prescribe multipie brands of each molecule in a
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`given month. Thus doctors do not specialize in particular drugs over time
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`or at a particular point in time.
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`Patients are not limited to a singie brand either. At a given point, 40%
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`of them will have had experienced a shift to a new brand of the same
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`molecule. Different doctors treat the same patient difierentiy. Aithough
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`the proportion of switches in the overall
`sample is 4.5%, among
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`observations where patients change doctors, the proportion rises to 9%.
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`This latter incidence, however, is much tower than the 48% switch rate that
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`we would expect were the new doctor not to take into account the patient’s
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`history of prescriptions, and to prescribe to the patient according to the
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`same proportion used for her other patients.
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`Most strikingiy, of those patients who were switched by the new doctor
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`and then returned to their originai doctor, 50% (44! 88) are switched again
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`when they go back to their nsuai doctor, and almost ail of those (93.2%
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`or 4E out of 44) go back to the treatment they received in the previous
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`period. These patterns demonstrate clearly that the probability of receiving
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`a new treatment is significantiy influenced by the doetor’s identity, and
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`that doctors differ in their choice among therapeutically equivalent drugs
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`for the same patient. Next, I present a formal econometric modei which
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`accommodates all these aspects of behaviour.
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`IV.
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`EMPIRICAL ANALYSIS
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`iV(i). Theory
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`I analyze the problern facing a doctor trying to choose among brands of
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`a certain molecule. The entire anaiysis is conditional on the choice of the
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`molecule, which is driven by a more complicated set of factors (indications,
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`patient’s general health, side efi"ects, price, etc); most of these factors are
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`unobservable to the econornetrician. By restricting myseif to the analysis
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`of ‘homogeneous’ goods, I can hoid price and quality constant and focus
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`on other factors driving the choice of competing brands?
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`The centrai focus of the study is the question of what brand of a given
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`moiecuie doctor i prescribes to a patient j given this patient’s past
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`prescription experience with this molecule. In particular, doctor i must
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`decide whether to prescribe the brand the patient received in the previous
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`period (hereafter o for ‘oid’ brand) or a new brand, which might generate
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`71 do not anaiyze whether the doctor prescribed a different presentation form, because all
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`the producers sail the same presentation forms, and there was therefore no need to change
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`vendor if the doctor or the patient wanted a different presentation form.
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`GD mackwcli Publishers Ltd. 2600.
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`Page 6 of21
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`Page 6 of 21
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`PREFERENCES IN THE PRESCRIPTEON DEClSION
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`(hereafter n for ‘new’ brand). More
`a higher utility to the doctor
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`specifically, a choice problem where the decision maker decides either to
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`stay on the diagonal of a transition probabilities model, or to move off-
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`diagonai to any other brand, is collapsed into a simpler problem where the
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`decision-maker is confronted with the binary choice of either to stay on
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`the diagonal or to move off-diagonal. Since the analysis is conditional on
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`the choice of the molecule, ‘new brand’ does not incinde other molecules.
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`355
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`I\/(ii). Estimation Strategy
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`The empirical model pararneterizes the probability of switching brands as
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`a function of patient and doctor attributes. I define as an ‘old’ brand, 0, at
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`time t, the brand consumed by the patient at time t— 1. This means that
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`1 define as a ‘new’ brand, any brand that differs from the ‘old’ one without
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`taking into account whether the ‘new’ brand was previously prescribed.
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`This means that my anaiysis focuses on ‘first~order’ state dependence, so
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`that only one—period lags have an effect. Moreover, since only the year and
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`month of the prescription are observed, there are ‘ties’ in the dataset. That
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`is, there are patients who receive more than one prescription in a. month.
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`When the prescriptions are ranked in a chronological order,
`the pre-
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`scriptions where the patient, molecule and month are the same are
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`randomly ordered. Finally, the prescriptions in the sample are written
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`either for one package or for two packages of the same brand. The sample
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`is evenly split between these two occurrences. Since I model a prescription
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`episode, and not quantity, as my dependent variable, I do not distinguish
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`between a prescription of two packages and a prescription of one
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`I use probit specifications to test the null hypothesis of no doctor and/or
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`patient preferences. These models include doctors’
`fixed effects and
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`patients’ random effects to capture the unobserved (to the econornetrician)
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`component of doctors’ and patients’ preferences.
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`The dataset suliers from the problem of initial conditions common to
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`most dynamic panel data models.
`I observe a sample of doctors and
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`patients for three years, but
`I do not have any information on their
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`behavior before the sample period begins. Ideaiiy, I would observe the
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`doctors’ behavior
`they started practicing and the patients’
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`prescriptions since they were first
`treated for ulcer problems. This is
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`impossible;
`therefore foilowing, one of the suggestions put forth in the
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`literature (I-Ieckman [l98l]), I assume that the prescription process starts
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`anew if patients have not had a prescription for six months. Thus, I only
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`“This implies a two—package prescription leads to the same degree of state-dependence as
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`a one-package prescription.
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`to Blackwell Publishers Ltd. ‘.2000.
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`ANDREA COSCELLI
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`use data on patients where I am able to observe whether for six months
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`prior they did or did not receive ulcer medications. This basically means
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`that when the process restarts,
`the decision makers do not retain the
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`information on anything which happened before.9
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`EV/(iii). Definition of the Variables
`Tables I and II list all the variables used in the estimations. SWITCH is
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`the dependent variable and it takes value 1 if patient j receives a brand at
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`time 5 different from what he received at time I — 1 (for the same molecule),
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`it is 0 otherwise. We review those variabies that are not seltlexplanatory.
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`Patients’ Variables
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`Time—z‘nvarz'ant #PRESCRIPTiONS distinguishes among patients ac-
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`cording to the seriousness of their ulcer probiem (e.g., chronic versus
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`TABLE I
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`PA’l‘!ENT—LEV£L VARIABLES useo IN THE ESTIMATION
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`Takes value 3 if the brand prescribed is different from the
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`brand previously prescribed, 0 otherwise
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`Takes value 1 if female and 2 if maie
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`Patient’s age
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`Total number of prescriptions that the patient receives in
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`the sample
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`Tolai number of different physicians who prescribed at
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`least one drug to the patient
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`Total number of different molecules that the patient
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`consumes in the sample
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`Number of prescriptions of the molecule up to time t
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`Number of (witiiimmoleeule) switches up to time t
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`Actual number of months elapsed between the prescription
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`at time t and the one at t - E
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`Dummy equal to 1 if the physician is a temporary one For
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`the patient, 0 otherwise
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`Dummy equal to 1 if the physician is a new permanent one
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`for the patient, 0 otherwise
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`Dummy equal to 1 if the patient returns to a previous _
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`physician, 0 otherwise
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`Dependent variable
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`SWITCH
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`Patients variables
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`GENDER
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`AGE
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`#PRESCRIP'§‘lONS
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`#DOC'i"0RS
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`#MOLECULES
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`#SPBLL«MOLECULE
`#PAS'F SWITCHES
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`.#MON’l‘l-IS
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`NEZWDOCTORJEMP
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`NEWDOCTOR-PERM
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`NEWDOCTOR—RBT
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`"'Mos: of the models were rerun with a 3-month window instead. The results are not
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`qualitatively dil°l'erent.
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`© Blackweil Publishers Ltd. 2000.
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`Page 8 of21
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`Page 8 of 21
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`PREFERENCES IN THE PRESCRIPTION DECISION
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`TABLE Ii
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`DOCTOR-LEVEL VARIABLES ussn IN ms Esrnvsmon
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`Doctors’ ChtII‘tICtt3riSt'iCS—artIi-uicet‘ market
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`QUANTITY
`Average monthly quantity prescribed by the doctor in the entire
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`market in the previous six months
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`Average monthly hcrfindahl index across prands in the entire
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`market in the previous six months
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`Average monthly herfindahl index at the molecule Eevel in the
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`entire market in the previous six months
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`H ER FBRAND
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`HERFMOLE
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`Doctors ’ characteristicswmolecitie-specific
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`MOLESHARE
`Share of prescriptions of the molecule by the doctor in the
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`previous month
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`Weighted proportion of the prescriptions of the molecule written
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`for the oid brand in the 2 previous months {last month's share
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`plus 09 oi‘ the previous rnontifs share}
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`% OLD BRAND
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`occasional), while #MOLECULES differentiates patients according to
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`their willingness to change treatment. #DOCTORS controis for patient-
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`spectfic preferences for Changing doctor. ?atients who change doctors
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`more often probably gather more information on possibic treatments;
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`therefore it ought to be more difiicnlt for a doctor to switch them; on the
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`other hand, these patients might be more experimental.
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`Ttmevarying There are three tt'me~varyt'ng covariates for the patient,
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`which are crucial to measure patient ‘habit’.
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`#SPELL~MOLECULE increases over time with any prescription of
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`the molecule. #PAST SWITCHES increases oniy upon a withiwmolccule
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`switch. These two ad~hoc variabies proxy for the switching tendency of
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`a patient. For example, a patient who receives the tenth prescription of
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`the molecuie ranitidine, has #SPELL—MOLECULE== 10,
`if #PAST
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`SWITCHES m0, this indicates that the patient has aiways been with the
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`same brand. Finaiiy, #MON'I”HS counts the actual number of months
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`elapsed between prescription episodes. it cannot exceed six, given the
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`definition of a treatment episode. Finally, there is a series of prescription-
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`specyic dummies defining whether the patient is receiving the prescription
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`from 21 Substituting physician (NEWDOCTOR-TEMP),]° has permanently
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`moved to a new physician (NEWDOC’I‘OR~PER1\/I), or is returning to
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`the usual physician (NEWDOCTOR-RBT) after receiving a prescription
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`from a substitute. These variables expiore whether doctors have diiferent
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`preferences for vendors of a particular molecule. If prescriptions decisions
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`‘O Most of the temporary substitu