SENJU EXHIBIT 2142
`LUPIN v SENJU
`IPR2015-01105
`
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`

`
`350
`
`ANEREA coscsLLr
`
`that tries to explain observed market segmentation using data on national
`market shares. Empirical observations of market shares for trade—name
`and generic drugs in post—patent therapeutic categories in the US market
`usually indicate a degree of segmentation between branded drugs and their
`generic equivalents, arising from a finite cross-price elasticity between the
`two types of drugs (the cross~pricc elasticity between two homogeneous
`goods
`should be infinite). However,
`these studies
`ignore individual
`heterogeneity. The micro dataset at hand allows me both (i) to control for
`individual heterogeneity and (ii) to explore the degree of time—dependence
`in drug choices, both of which can be important
`in explaining the
`substantial and persistent differences in market shares among therapeuti-
`cally equivalent drugs.
`Second, in recent years, we have witnessed a surge in direct advertising
`to consumers by pharmaceutical companies for prescription drugs sold in
`the US market. The amount spent on direct-to-consumer prescription drug
`advertising rose from US$35rn in £987 to US$357rn in 1995, US$6l0m in
`1996, and over USS} billion in 1997 (NERA {i999}). This spending choice
`reflects a widespread belief within the pharmaceutical
`industry that
`patients should have a role in the choice of prescrr'ptz'on drugs. This paper
`directly studies the patient’s role in pharmaceutical choice.
`Finally, the most important institutional features of the Italian market
`during the sample period such as the important role of licensed products,
`limited patient co—payrnents, and lack of direct financial
`incentives to
`doctors to prescribe cheaper drugs characterize almost every EU country
`(NERA {l999}).
`I use a new panel dataset provided by the Etalian National Health
`Institute, which includes all the prescriptions in the anti-ulcer market from
`l990—i992 for a 10% random sample of the population of Rome aged
`lS»85. This dataset allows researchers a glimpse into the dynamics of
`prescription behavior at the micro level which is not possible with the
`predominantly aggregate and! or cross~sectional datasets which have been
`used in most studies of pharmaceutical markets to date.
`My main conclusions are as follows. I begin by testing the null hypo-
`thesis of whether doctors and.’ or patients are indifferent between drfilerent
`brands of the same molecule, as we would expect given their therapeutic
`equivalence. After rejecting the hypothesis, I attempt to isolate both the
`patient-level and the doct0r—level factors which are responsible for product
`differentiation.
`I focus specifically on the degree of time—dependence in
`doctors’ and patients’ drug choices by testing whether the patients show
`state dependence in their purchasing patterns, and whether the doctors
`exhibit habit persistence. I find significant evidence of doctor and patient
`‘habit’, which imply that in molecular sub markets in which brands are not
`allowed to compete on the basis of price, habit persistence at the micro
`
`£9 Blackwell Publishers Ltd. 2006.
`
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`PREFERENCES IN ms PRESCRIPTION DECISION
`
`351
`
`level can translate into sticky and persistent market shares at the aggregate
`level.
`
`'¥."he paper is organized as follows. In the next section I survey the
`previous empirical literature. In Section ill, I describe the dataset used in
`the estimation. Section IV describes my empirical specification, while
`Section V reviews the results. A summary of the results make up the final
`section.
`
`:1. Docrons’ DEMAND
`
`While the present study focuses on doctors’ demand for pharmaceutical
`products, most of the recent literature on pharmaceuticals (for example,
`Caves at of. H991], Caves and I-Iurwitz [I988], Berndt et at’. {£997}, Scott
`Morton {l99’7, 2000], and Scherer [l993]) has focused on supply—side issues
`(e.g., entry, pricing, advertising, R&D races). In his comment on Caves
`at
`:11.
`{I991}, Fakes
`[i991] argues
`that a panel
`following doctors’
`prescriptions over time would be the only way to understand the major
`determinants of the demand For pharmaceuticals. The panel data E use
`aiiow me to separately identify doctor and patient effects.
`Much of the previous work on the demand for pharmaceuticals has used
`aggregate, market—share data, which are much better suited to measuring
`the degree of differentiation between various drugs rather than explain its
`causes. For example, Stern {l995]
`finds low substittitability between
`branded and generic drugs, while Eliison er a1. {I997} find a high elasticity
`of substitution between generic and branded drugs.
`One recent microdata-based analysis of the demand for pharmaceuticals
`is that by Hellerstein U998}. She focuses on doctors’ choices between
`branded and generic versions of drugs for which a patent has recently
`expired. Significantly, she finds some evidence of habit persistence in the
`prescription behavior of physicians, even after controlling for observable
`characteristics of physicians and patients. Unfortunately, her dataset does
`not allow her to test for patients’ effects owing to data limitations, while
`her dataset allows for an analysis of financial incentives due to third—party
`payer variation. My dataset, on the other hand, has multiple observations
`for doctor-patient interactions, prescription of the same moiecule by a
`single doctor to many patients, and prescriptions of the same drug by
`many doctors.
`£987} analyzes competition between patent holders
`Gorecki E1986,
`and licensees in Canada: an institutional setting very similar to the
`Italian market. He only observes aggregate data, but he is able to take
`advantage of the regulatory variation among Canadian provinces to
`identify
`competitive
`effects
`in
`his
`empirical
`anaiyses. Gorecki’s
`conclusions in [E9863 are consistent with my results: ‘. .. Since physicians
`still write, by and large, brand name prescriptions for the pioneering
`£133 Blackwell Publishers Ltd. 2000.
`
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`
`352
`
`ANDREA COSCELLE
`
`brand, unless an element of price competition is introduced at the level of
`the pharmacist the pioneering brand will continue to dominate the market
`[. . .]. Hence it
`is
`the combination of attempting to nullify quality
`differences between the pioneering and late
`entrant brands
`and
`introduction of price competition that results in the late entrants capturing
`market share’.
`
`Pharmaceutical markets are subdivided into therapeutic classes. Follow-
`ing most of the recent economic literature on pharmaceuticals {e.g., Stern
`[l995]), I regard a therapeutic class as having several sub-markets. I define
`a therapeutic market as a 4-digit ATC code (for example, A028 contains
`all the anti-ulcer drugs), and a sub—market as a specified molecule (for
`example,
`ranttidine). The ATC code is an international classification
`scheme which classifies drugs by target part of the anatomy, mechanism of
`action, and chemical and therapeutic characteristics. This is a natural
`definition of demand because a 4~digit ATC code includes all the molecules
`which can theoretically be prescribed for a certain diagnosis. The
`molecules themselves difi"er according to side effects,
`interactions with
`other drugs, specific indications and prices. In the markets I study, a
`physician typically decides the appropriate molecule for the diagnosis and
`then she decides which trademamel version of the molecule to prescribe to
`the patient.
`My work focuses on a particular therapeutic market: anti—uicer drugs
`(A023).
`I analyze this market because it accounts for a considerabie
`proportion of world-wide expenditure on pharmaceuticals (around 5%,
`IMS International [l996}). Ulcers also required repeated treatment in the
`early 1990s,” a key feature of my analysis.
`I analyze six molecule
`submarkets (famotidine, ranitidtrze, nizatidine, roxatidine, omeprazole and
`misoprostole), which represent more than 90% of the prescriptions during
`the sample period (l990—}992). I restrict my sample to these six molecules
`because the other molecules represent more ‘mature’ and smaller sub-
`marlcets, where some of the prices for identical brands dii‘l‘er.3 In each sub-
`market
`there is a patenbholder and licensees marketing the molecule.
`Much of the literature on trade-name drugs versus generics is concerned
`with the dimensions
`(among others,
`‘perceived quality’ or name
`recognition) according to which these products ‘differ’. In my analysis I
`focus on competing drugs based on the same active ingredient and
`
`‘All the drugs sold under a iicense or a patent in the ltalian market have a trade name.
`alt has recently been found that approximately 80% of peptic ulcers can be cured by
`eradicating He.!'1'c0bap£el‘ Pylori, a bacterium responsible for the recurrence of ulcers, by using
`a combination of antibiotics and antimulcer drugs (Graham [§993l).
`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-
`maricets are very small firms for whom the assumption ofidentical quality might not hold.
`© Blackwell Publishers Ltd. 2000.
`
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`PREFERENCES IN THE PRESCRIPTION EDIZCHSION
`
`353
`
`marketed by important producers entering the market at
`time.‘‘
`
`the same
`
`III.
`
`THE DATA
`
`The main dataset (provided by the Istitato Superiore della Samlta ’) records,
`for a 10% sample of the population of the Metropolitan Area of Rome
`aged 15-85, all the prescriptions in the anti-ulcer (A0213) drug market
`during the period E990»~l992. The sample is stratified according to age and
`gender; so that the results are representative of the Rome population.
`This patient-level dataset contains over 3I0,000 observations. An obser~
`vation records the identity of the prescribing doctor, the identity of the
`patient, the year and month, and the particuiar presentation form of the
`drug prescribed (for exarnpie,
`1 package of ZANTAC 20 tablets,
`i50 mg
`each). An observation indicates exactly the drug bought by the patient,
`because the records are collected from pharmacies. In the patient-ievel
`dataset there are more than 3,400 doctors prescribing at least once to one
`of the in-sample patients. A supplementary dataset from the same source
`records ail the prescriptions that 350 of these doctors wrote for any of
`their patients during the same period. The supplementary cioctor—based
`dataset contains over 710,000 prescriptions and each observation records
`exactly the same information as the patient—ievel ctataset. The final dataset
`used in my estimations has more than 75,000 observations; it retains all
`the observations in the patientdevei dataset for the patients who received
`at
`ieast one prescription from one of the 350 doctors whose entire
`prescription history is known.
`
`Italian Marker Three important characteristics of the Italian pharmau
`ceutical industry are: (i) there is no price and third-party payer variation,
`(ii) the over—the—connter (OTC) market was tiny in the period of interest,
`and direct advertising to patients for prescription drugs had not yet
`started,5 and (iii) during the sample period, the pharmacist had no power
`to sabgtitute generics for trade-name drugs, as he does in many American
`states.
`
`Doctors’ Prescribing Behavior Doctors heavily prescribe across brands:
`
`“By doing this I believe I have effectively controlled for all ‘objective’ dimensions of
`difierentlation between drugs; therefore I can proceed with my tests of doctor or patient
`indifference fairly confident
`that
`I have controlled for a large share of drug-specific
`heterogeneity.
`5 It is currently illegal throughout the EU and will remain so for several years even though
`the subject is now occasionally raised.
`5Hellersteir2‘s dataset, therefore, potentially contains a iarge amount of measurement error
`in the prescription variable for states where substitution with generics is mandatory.
`© Blackwell Publishers Ltd. 2000.
`
`PAGE 5 OF 21
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`354
`
`ANDREA COSCELLI
`
`for example, 98.6% of the in—sarnple doctors prescribed each of the three
`avaiiable brands of omeprazole at ieast once during the sample period.
`Moreover, doctors usually prescribe multipie brands of each molecule in a
`given month. Thus doctors do not specialize in particular drugs over time
`or at a particuiar point in time.
`Patients are not Eimited to a single brand either. At a given point, 40%
`of them wiil have had experienced a shift to a new brand of the same
`moiecule. Diiferent doctors treat the same patient dilierently. Although
`the proportion of switches in the overall
`sample is 4.5%, among
`observations where patients change doctors, the proportion rises to 9%.
`This fatter incidence, however, is much Eower than the 48% switch rate that
`we would expect were the new doctor not to take into account the patient’s
`history of prescriptions, and to prescribe to the patient according to the
`same proportion used for her other patients.
`Most strikingly, of those patients who were switched by the new doctor
`and then returned to their original doctor, 50% (44! 88) are switched again
`when they go back to their usual doctor, and almost all of those (93.2%
`or 41 out of 44) go back to the treatment they received in the previous
`period. These patterns demonstrate cieariy that the probability of receiving
`a new treatment is significantiy influenced by the doctor’s identity, and
`that doctors differ in their choice among therapeutically equivaient drugs
`for the same patient. Next, I present a formal econometric rnodei which
`accommodates all these aspects of behaviour.
`
`IV .
`
`EMPIRICAL ANALYSIS
`
`iV(i). Theory
`
`I analyze the problem facing a doctor trying to choose among brands of
`a certain moiecule. The entire analysis is conditional on the choice of the
`molecule, which is driven by a more complicated set of factors (indications,
`patients general health, side efiects, price, etc.); most of these factors are
`unobservable to the econometrician. By restricting myseif to the analysis
`of ‘homogeneous’ goods, I can hoid price and quality constant and focus
`on other factors driving the choice of competing brands.7
`The central focus of the study is the question of what brand of a given
`molecule doctor i prescribes to a patient j given this patient’s past
`prescription experience with this moiecuie. In particular, doctor i must
`decide whether to prescribe the brand the patient received in the previous
`period (hereafter o for ‘old’ brand) or a new brand, which might generate
`
`71 do not anaiyze whether the doctor prescribed a different presentation form, because all
`the producers sell the same presentation forms, and there was therefore no need to change
`vendor if the doctor or the patient wanted a different presentation form.
`it? Btackwcli Prihlislici-s Ltd‘ 2600.
`
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`
`PREFERENCES IN THE PRBSCRIPTEON DECISION
`
`355
`
`r: for ‘new’ brand). More
`(hereafter
`a higher utility to the doctor
`specificaiiy, a choice problem where the decision maker decides either to
`stay on the diagonal of a transition probabilities model, or to move off-
`diagonai to any other brand, is collapsed into a simpler problem where the
`decision-maker is confronted with the binary choice of either to stay on
`the diagonal or to move ofiidiagonal. Since the analysis is conditional on
`the choice of the molecule, ‘new brand’ does not include other molecules.
`
`IV(ii). Estimation Strategy
`
`The empirical model pararneterizes the probability of switching brands as
`a function of patient and doctor attributes. I define as an ‘old’ brand, 0, at
`time t, the brand consumed by the patient at time t— 1. This means that
`I define as a ‘new’ brand, any brand that differs from the ‘old’ one without
`taking into account whether the ‘new’ brand was previously prescribed.
`This means that my analysis focuses on ‘firsborder’ state dependence, so
`that only one-period lags have an effect. Moreover, since only the year and
`month of the prescription are observed, there are ‘ties’ in the dataset. That
`is, there are patients who receive more than one prescription in a month.
`When the prescriptions are ranked in a chronological order,
`the pre-
`scriptions where the patient, molecule and month are the same are
`randomly ordered. Finally, the prescriptions in the sample are written
`either for one package or for two packages of the same brand. The sample
`is evenly split between these two occurrences. Since I model a prescription
`episode, and not quantity, as my dependent variabie, I do not distinguish
`between a prescription of two packages and a prescription of one
`package.3
`i use probit specifications to test the null hypothesis of no doctor and! or
`patient preferences. These models include doctors’
`fixed effects and
`patients’ random effects to capture the unobserved (to the econometrician)
`component of doctors’ and patients’ preferences.
`The dataset sulfers from the probiem of initial conditions common to
`rnost dynamic panel data models.
`I observe a sample of doctors and
`patients for three years, but
`I do not have any information on their
`behavior before the sample period begins. Ideally, I would observe the
`doctors’ behavior
`since
`they started practicing and the patients’
`prescriptions since they were first treated for ulcer problems. This is
`impossible;
`therefore following one of the suggestions put forth in the
`literature (Heckrnan {i98l}), I assume that the prescription process starts
`anew if patients have not had a prescription for six months. Thus, I only
`
`“This implies a two—paclcage prescription ieads to the same degree of state-dependence as
`a one—package prescription.
`© Blackwell Publishers Ltd. 2000.
`
`PAGE 7 OF 21
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`
`356
`
`ANDREA COSCELLI
`
`use data on patients where I am able to observe whether for six months
`prior they did or did not receive ulcer medications. This basically means
`that when the process restarts,
`the decision makers do not retain the
`information on anything which happened before.9
`
`El/(iii). Definition of the Variables
`
`Tables I and II list all the variabies used in the estimations. SWITCH is
`
`the dependent variable and it takes value 1 if patient j receives a brand at
`time t different from what he received at time t —— 1 (for the some moiecule),
`it is 0 otherwise. We review those variables that are not seifiexpianatory.
`
`Patients’ Variables
`
`Timewinvarianz #PRESCRIPTiONS distinguishes among patients ac»
`cording to the seriousness of their uicer probiem (e.g., chronic versus
`
`TABLE I
`P.-U‘!ENT—LEV£L VARIABLES USED IN ‘rm: ESTIMATION
`
`Dependent variable
`SWITCH
`
`Patient is variables
`GENDER
`AGE
`
`.#PRESCRlPTIONS
`
`#DOCTORS
`
`#MOLECULES
`
`#SPF:LL—MOLECULE
`#“PAS'F SWITCHES
`.#MONTHS
`
`Takes value 1 if the brand prescribed is different from the
`brand previously prescribed, 0 otherwise
`
`Takes value 1 if female and 2 if‘ male
`
`Patient’s age
`Total number of prescriptions that the patient receives in
`the sample
`Tolai number of different physicians who prescribed at
`least one drug to the patient
`Total number of different molecules that the patient
`consumes in the sample
`Number of prescriptions oi" the molecule up to time t
`Number of (witiiimmolecule) switches zip to time t
`Actual number of months elapsed between the prescription
`at time t and the one at t —— i
`
`NEWDOCTOPJFEMP
`
`NEWDOCTOR-PERM
`
`Dummy equal to 1 if the physician is a temporary one for
`the patient, 0 otherwise
`Dummy equal to i if the physician is a new permanent one
`for the patient, 0 otherwise
`NEWDOCTOR—RET
`Dummy equai to 1 if the patient returns to a previous
`physician, 0 otherwise
`
`9Most of‘ the models were rerun with a 3«month window instead. The results are not
`quaiitatively different.
`© Blackwcil Publishers Ltd. 2000.
`
`PAGE 8 OF 21
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`
`PREFEIUENCES IN ms PRESCRIPTION DECISION
`TABLE Ii
`DOCTOR-LEVEL VAREABLES useo IN THE ESTIMATION
`
`357
`
`Doctors’ Chfirtlct‘tZt'iSt'iiC.S‘—-{mill-uticet‘ market
`QUANTYTY
`Average monthly quantity prescribed by the doctor in the entire
`market in the previous six months
`Average monthly herfindahl index across brands in the entire
`market in the previous six months
`Average monthly herfindah] index at the molecule level in the
`entire market in the previous six months
`
`E-IERFBRAND
`
`HERFMOLE
`
`Doctors ’ chamctertstics»~m0lecu1e~.!'pec{fic
`MOLESHARE
`Share of prescriptions of the molecule by the doctor in the
`previous month
`Weighted proportion of the prescriptions of the moiecule written
`for the oid brand in the 2 previous months {last month's share
`plus 09 oz" the previous rnonth’s share)
`
`% OLD BRAND
`
`occasional), while #MOLECULES differentiates patients according to
`their willingness to change treatment. #IDOCTORS controis for patienb
`specific preferences for changing doctor. ?atients who change doctors
`more often probably gather more information on possibie treatments;
`therefore it ought to he more difficult for a doctor to switch them; on the
`other hand, these patients might be more experimental.
`
`Tz'me-varying There are three time-varying covariates for the patient,
`which are crucial to measure patient ‘habit’.
`#SPELL~MOLECULE increases over time with any prescription of
`the molecule. #1-"AST SWITCHES increases only upon a withirwnoicculc
`switch. These two ad~hoc variables proxy for the switching tendency of
`a patient. For example, a patient who receives the tenth prescription of
`the motecute ranitidine, has 4.‘¢SPELL—MOLECULE== 10,
`if #PAST
`SWITCHES m0, this indicates that the patient has aiways been with the
`same brand. Finaiiy, .#i\/IONTHS counts the actual number of months
`elapsed between prescription episodes. it cannot exceed six, given the
`definition of a treatment episode. Finally, there is a series of prescription-
`spectfic dummies defining whether the patient is receiving the prescription
`from a substituting physician (NEWDOCTOR—TEMP),’° has permanently
`moved to a new physician (NEWDOCTOR-PERM), or is returning to
`the usual physician (NEWDOCTOR—RBT) after receiving a prescription
`from a substitute. These variabies explore whether doctors have different
`preferences for vendors of a particuiar molecule. If prescriptions decisions
`
`“Most of the temporary substitutions occur when the physician is on vacation, or the
`patient is out of town when he needs a prescription.
`© Blackwell ?ublishcrs Ltd. 2009.
`
`PAGE 9 OF 21
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`358
`
`ANDREA ooscsrm
`
`are determined soieiy by the patient’s condition, a change in the pre-
`scribing doctor shouid not vary the patient’s likelihood of being switched.
`
`I use doctor covariates which are time-varying. Most
`Doctors‘ Variables
`of them summarize the prescribing behavior of the physicians in the six
`months imrnediateiy prior to the prescription episode.
`
`Constant across molecule markets Table II describes the variables in
`detail. QUANTITY distinguishes between heavy and tight prescribers of
`anti—ulcer drugs, and proxies for the size of the doctor’s patient base.
`HERFBRAND proxies for the doctors’ preference to prescribe multiple
`brands, while HERFMOLE controls for the dispersion in prescribing
`behavior of the doctor due to choice of difiereaz molecules, which is
`dictated by heterogeneity in the patients’ pool. Its coefficient does not have
`any specific economic meaning:
`it simply ailows me to interpret
`the
`coefficient on HERFBRAND as
`representing doctors’ preference for
`prescribing multipie brands, rather than their need to prescribe muitipie
`drugs (molecules) due to therapeutic considerations. This distinction is
`based on the assumption that the doctor first chooses the molecule that
`suits the patient and then decides which brand to prescribe.
`
`Moleculaspectfic % OLD BRAND captures habit persistence by the
`doctor. MOLESHARE proxies for the importance that the molecule has
`for the doctor.
`
`Molecule markers The number of competing products, the time since
`the molecule entered the market, the size of the vendors, and the amount
`of advertising all differ across molecule markets. I use moiecule dummies
`to proxy for the different competitive conditions in each molecule market.
`Finaily,
`I
`include monthly dummies to control for exogenous changes
`(such as regulatory changes) that occur during the sample period.
`
`Summary Statistics Table III provides sample summary statistics. There
`are siightly more than 75,000 observations in the original sarnpie, an
`observation being a prescription.
`I exclude the following observations:
`(i) patients who received prescriptions from doctors whose entire pre-
`scription history is not known, (ii) the first prescription of each molecule
`for each patient, so as to initialize the process, and (iii) ail prescriptions in
`the first 6 months and those more than 6 months apart for the same patient
`because of the initial conditions probiem discussed above. This leaves
`43,840 observations.
`There are more than 5,000 different patients and 350 doctors in the
`sample. I observe a change of brand (SWITCH) in 4% of the observations.
`There are siightly more prescriptions written to females in the sample,
`63 Blackwell Pubiishers Ltd. 2000.
`
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`
`PREFERENCES IN THE PRESCRIPTION DECISION
`TABLE III
`SUMMARY S'§'A’I‘iS'I‘ICS FOR THE SAMPLE
`
`359
`
`Max
`Std. Dev. Min
`Mean
`
`
`Dependent variable
`Switch
`
`Patient is variables
`Gender: 1 if fcmaie, 2 if maic
`Age
`.#In-sampie prescriptions
`#Molecu1es prescribed in—sampie
`#Different doctors prescribing
`#Prescriptions of the molecule
`#Past switches up to time I
`#Months between prescriptions
`New doctor—temp
`New doctorwperrn
`New doctor-ret
`
`0.040
`
`0.197
`
`0
`
`1
`
`'
`
`1.484
`62.73
`29.478
`1.87
`1.629
`i4.9S5
`0.396
`1.232
`0.018
`0.02%
`0.024%
`
`0.499
`12.65
`19.75
`1.09
`1.1 i
`12.68
`1.205
`1.277
`0.134
`0. E43
`0.155
`
`1-F
`15
`2
`1
`1
`2
`0
`0
`0
`0
`0
`
`2-M
`85
`126
`8
`14
`106
`18
`6
`E
`1
`1
`
`Doctors‘ cltaractcrEstics~anl£«m'cer market
`Average monthly quantity prescribed in»samp]e
`E-ierfindahl brand level
`Herfindahl rnolecirie Eevel
`
`Doctors ’ characteristicswmoiecule specific
`% Oid brand prescribed
`Molecule share
`
`876.168
`0.243
`0.485
`
`297.568
`0.069
`0.123
`
`207.08
`0.09
`0.292
`
`25 1 2.717
`0.68
`0.93
`
`0.97
`0.528
`
`0.405
`0.256
`
`0.025
`0.025
`
`1.9
`1
`
`Moleculc sub-market
`1
`0
`0.438
`0.742
`mnidine
`1
`0
`0.199
`0.04
`nizatidine
`1
`0
`0.091
`0.008
`roxatidme
`
`omeprazale l 0.097 0.297 0
`
`
`
`
`While the average age is around 62. Turning to the time—varying covariates
`for the patients: the average prescription is written to a patient who is
`purchasing the particular molecule for the fourteenth time (#SPELL-
`MOLECULE) and who has already switched brands 0.4 times (# PAST
`SWITCHES). The average time between two prescriptions (#MONTHS)
`is less than two months. Finaiiy, in approximately 6% of the prescriptions,
`the prescribing doctor differs
`from the one who wrote the previous
`prescription. The number of prescriptions where the patient temporarily
`visits a doctor difierent
`from the one responsible for
`the previous
`prescription {NEWDOCTOR-TEMP) is about 1.8%; whereas 2% of the
`prescriptions are written by a new doctor, who then becomes the usual
`doctor for the patient (NEWDOCT‘OR—PERM). Finally, 2.4% of the
`prescriptions are written by the osuai physician after the patient has been
`prescribed something by 21 substitute in the previous period.
`The unit of measurement for the quantity variable (QUANTITY) is
`the defined daily dose, which is an international measurement unit. For
`© Blackweil Publishers Ltd. 2000.
`
`PAGE 11 OF 21
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`

`
`360
`
`ANDREA COSCELLI
`
`each molecule, the daily number 01" milligrams of the chemical compound
`that the average patient needs is defined. Therefore any package can be
`transformed into a number of days of therapy for the average patient.
`Finally, the dummy variables for the molecule markets indicate that 74%
`of the prescriptions in the sample were for
`rcmitidine,
`foliowed by
`omeprazole with 9%.
`Yable IV compares the market shares in the original sample to those in
`the estimation samples. First, column (1) shows that withimmolecule
`market shares differ. Second, the main estimation sample is remarkably
`similar to the original sample, white the switching sample in column (3)
`
`TABLE IV
`Manner SHARES IN THE SAMPLE
`
`Colnmn (1) shows market shares in the overall sample; in column (2) market shares in the
`sample used in the estimation are reported. Column (3) is a subset of the sample used in
`column (2), conditional on the patients’ being prescribed a new brand. Column {4} indicates
`the expected brand frequency in the switch sarnpie based on the total in~sample market shares
`Estimation
`Overall
`Estimation
`sampie and
`Conditional
`
`sample
`sample
`switch
`probabilities
`
`Famotidfne
`FAMODIL
`GASTREDIN
`MOTIAX
`
`Ranitidine
`RANIBEN
`RANSBLOC
`RANIDIL
`TRIGGER
`Uacex
`ULKOBREN
`ZANTAC
`
`Nizaridine
`Caomz.-xr
`NIZAX
`ZANIZAL
`
`Roxalidine
`GASTRALGIN
`N501-X2
`Roxrr
`
`0.467
`0.448
`0.083
`
`0.02
`0.02%
`0.419
`0.021
`0.029
`0.002
`0.483
`
`0.359
`0.581
`0.059
`
`0.332
`0. E99
`0.468
`
`0.458
`0.453
`0.088
`
`0.03
`0.025
`0.432
`0.024
`0.024
`0.003
`0.467‘
`
`0.37%
`0.566
`0.062
`
`0.292
`0.161
`0.546
`
`0.415
`0.415
`0.169
`
`0.07
`0.062
`0.387
`0.032
`0.033
`0.005
`0.405
`
`0.437
`0.437
`0.125
`
`0.285
`0.428
`0.286
`
`0.437
`0.454
`0.108
`
`0.03§
`0.026
`0.435
`0.017
`0.026
`0.007
`0.454
`
`0.453
`0.466
`0.08
`
`0.3
`0.272
`0.427
`
`Omeprazoie
`0.304
`0.303
`0.343
`0.304
`LOSEC
`0.326
`0.37
`0.329
`0.352
`MEPRAL
`0.33
`0.326
`0.328
`0.343
`OMEPRAZEN
`
`#0bservations 1,775 75,316 43,840 1,775
`
`
`
`
`Source: Author‘s computations based on the data set described in Section III.
`© Blackwell Publishers Ltd. 2000.
`
`PAGE 12 OF 21
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`

`
`PREFERENCES IN THE PRESCRIPTION DECISION
`‘ TABLE V
`CORRELATION TABLE
`
`301
`
`
`
`
`
`
`SWETCI-I #SPMC)LVariable #1\/ION #PASTSW °/o0Li)§3R HERFB
`SWITCH
`1.000
`1.00
`#SPELL-MOLECULE
`~0.05
`1.00
`#'MON'I‘HS
`0.0587 —0.02332
`1.00
`-0.071’?
`#PAST SWITCHES
`0.1897
`0.2442.
`L00
`—0.09S9
`——0.361
`ah’-‘OLD BRAND
`-43.0983
`-0. 185
`1.00
`0.1788
`—-0.0323
`0.0022
`HBRFBRAND
`-0.012‘)
`~0.025'7
`
`QUANTITY ——0.0533 —~0.l072 0.02 —~0.0525 0.027i 0.0066
`
`
`
`
`Source: Author’s computations based on the data set described in Section III.
`
`shows the small brands receiving a somewhat Earger share than in the other
`sample. Cohrmn (4) computes the conditional probabilities of a drug being
`prescribed upon switching,
`i.e.,
`I assume that the probability of being
`prescribed brand i, given that a patient is switched from j is simpiy brand
`i’s share among ail brands other than j in the sample, and assuming that
`the probability of being switched away from brand j is the same for ail j.
`The comparison of column (3) and column (4)
`indicates that srnalier
`brands (such as NEOH2, MOTIAX and ZANIZAL) gain market share from
`the larger brands by having patients who have already received the
`molecule switched to their drugs.“ Finaily, there is not any significant
`increase in market shares for the small producers during the sample
`period, even if the subsample of the ‘new patients’ is separately analysed.
`
`Correlations Table V provides the sample correlations for the duration
`covariates measuring doctor and patient habit persistence.
`to
`Table V indicates habit persistence by doctors (doctors tend not
`switch patients whose ‘old’ drug is what they usuaiiy prescribe) and brand-
`loyalty by patients (patients are not switched after they have received the
`brand for a certain number of consecutive periods).
`
`V.
`
`RESULTS
`
`V(i). Baseline Model
`
`The first column in Table VI presents the estimation results for the
`baseline rnodei, which uses all the information availabie.
`GENDER and AGE are both significant. Men are prescribed a new
`
`" The difference between the expected frequency computed in coiumn (4), and the actual
`frequency reported in column (3)
`is quite limited considering that the actuai number of
`observed switches to small drugs in the sample is very limited for the ieast
`important
`molecules:
`for example there are oniy three switches to N201-I2, and two switches to
`ZANIZAL.
`© Blackwell Publishers Ltd. 2000.
`
`PAGE 13 OF 21
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`

`
`362
`
`ANDREA COSCELLI
`T.uu.x-: VI
`BASELENE Moos1.mPaosrr Esrizvmriou
`
`Asymptotic t-statistic on the right-hand side
`
`Dependent variabie—SWITCH
`
` Baseline No patients No doctors
`
`
`
`
`Patients ‘ char-actermics
`-2.96
`~0.07l
`Gender: 1 if1'ema1e,2i1‘ma!e
`3.03
`0.003
`Age
`-1.66
`Totaé #in—samp1e prescriptions —-0.002
`3.27
`Total #moEccu1es prescribed
`0.040
`#S;)eil-molecule
`-0.026 W12. 19
`#Past switches
`0.244
`32.77
`#Months
`0.069
`8.3% 1
`.#Difi‘erent doctors
`0.011
`0.989
`New doctor-temp
`0.108
`L083
`New doctorqierm
`0.344
`5.328
`New doctor-ret
`0.081
`1. 224
`Newdoc x #spe1E-molecule
`0.03 3
`2.030
`
`~—0.07
`0.002
`-0.003
`0.066
`0.025
`0.23
`0.08
`0.012
`0.342
`0.312
`0.059
`0.0006
`
`-3.43
`3.02
`——3.068
`6.33
`—— 2 3.55
`36.568
`1i.34
`1.329
`5.934
`6.454
`1.046
`0.166
`
`Doctors ’ characterz'.ru'cs——arzti-ulcer marker
`Total quantity prescribedf 100
`0.002
`Herfindahi brand leveé
`~0.337
`Herfindabl molecule level
`0.079
`
`0.86
`-2.48
`0.79
`
`