THE JOURNAL OF INDUSTRIAL ECONOMICS
`Volume XLVIII
`September 2000
`
`0022- i 821
`No. 3
`
`THE IMPORTANCE OF DOCTORS’ AND PATIENTS’
`PREFERENCES IN THE PRESCRIPTION DECISION*
`
`ANDREA CoscaLLi’r
`
`This paper studies the contribution of doctor and patient ‘habit‘ to
`persistence in market shares in prescription drug markets. My unique
`panel dataset allows me to estimate the probability of switching brands
`as a function of patient and doctor attributes, with an emphasis on
`past prescribing behaviour so as to capture the degree of persistence. I
`find significant evidence of time—dependence in prescription choices
`for both doctors and patients, which seems to imply that in molecular
`submarkets in which brands are not allowed to compete on the basis of
`price, doctor and patient ‘habit‘ at the micro—level can translate into
`sticky and persistent market shares at the aggregate level.
`
`1.
`
`INTRODUCTION
`
`{N THIS PAPER, I study the contribution of doctor and patient ‘habit’ to
`persistence in market shares among therapeutically equivalent prescription
`drugs. While, similar issues have arisen in the recent literature about the
`competition between generic and branded drugs,
`they are especially
`puzzling in the Italian pharmaceutical market.
`in Italy, regulatory fiat
`imposes uniform prices across all vendors of drugs which utilize the same
`active ingredient, thus eliminating price variation as an important avenue
`of differentiation among otherwise therapeutically equivalent drugs, which
`is true in drug markets with generic competitors. My unique panel dataset
`allows me to estimate the probability of switching brands as a function
`of patient and doctor attributes, with an emphasis on past prescribing
`behaviour so as to capture the degree of persistence.
`This analysis can shed, light on several aspects of market structure in
`the pharmaceutical industry. First, there is a growing body of literature
`
`from the European Commission through a TMR
`*I acknowiedge financial support
`fellowship #ERBFMBICT972232. i would liite to thank the Istituto Superiore dz‘ Saniza’ for
`the use of their data. This paper was previously circulated under the title ‘Are Market Shares
`in Drug Markets Affected by Doctors’ and Patients‘ i’relerences for Brands?'. Seminar
`participants at Stanford University (GSB and Department of Economics), Royal Holloway
`and UCL have provided valuable comments.
`I would like to especially thank my principal
`adviser, Peter Reiss, who constantly helped me improve this paper, and the editor David
`Genesove for his useful comments. Mike Mazzeo, Fiona Scott Morton, Andrea Shepard,
`Matthew Shum and an anonymous referee have also provided many useful suggestions. All
`errors, however, are my own.
`1'Anthor‘s affiliation: National Economic Research Associates, l5 Stratford Place, London
`W] N QAF, UK.
`email: Andrea. Cosceiii@nem. com
`o Blackwell Publishers {.112000. :05 Cowley Road, Oxford OX4 15?. UK. and 130 Main Slrccl. Malden. MA 02143. USA.
`349
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`350
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`ANDREA COSCELLI
`
`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~price elasticity between two homogeneous
`goods should he 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$35m in 1987 to US$357m in 1995, US$610m in
`1996, and over US$1 billion in 1997 (NERA [1999]). This spending choice
`reflects a widespread belief within the pharmaceutical
`industry that
`patients should have a role in the choice of prescription 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 [1999]).
`I use a new panel dataset provided by the italian National Health
`Institute, which includes all the prescriptions in the anti-ulcer market from
`i990~1992 for a 10% random sample of the population of Rome aged
`1585. 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 crosswsectional 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 dr'fibrent
`brands of the some molecule, as we would expect given their therapeutic
`equivalence. After reiecting the hypothesis, I attempt to isolate both the
`patient—level and the doctor—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~
`
`(it Blackwell Fublishers Ltd. 2600.
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`PREFERENCES as ran PRESCRIPTION DECISION
`
`35E
`
`level can translate into sticky and persistent market shares at the aggregate
`level.
`
`The 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.
`
`it. Decreas’ DEMAND
`
`While the present study focuses on doctors’ demand for pharmaceutical
`products, most of the recent literature on pharmaceuticals (for example,
`Caves er a1. [l99i}, Caves and Hurwitz {1988}, Berndt er al. [1997], Scott
`Morton [1997, 2000}, and Scherer {I993D has focused on supply-side issues
`(cg, entry, pricing, advertising, R&D races). In his comment on Caves
`er
`a1.
`[1991}, Fakes E199i} 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 1 use
`allow 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 [1995]
`finds low substitutability between
`branded and generic drugs, while Ellison er a1. [1997} find a high elasticity
`of substitution between generic and branded drags.
`One recent microdata-based analysis of the demand for pharmaceuticals
`is that by Hellerstein {1998]. 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 centrolling 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 oi" the same molecule by a
`single doctor to many patients, and prescriptions of the same drug by
`many doctors.
`I987] analyzes competition between patent holders
`Gorecki
`[1986,
`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
`analysas. Gorecki’s
`conclusions in [1986] are consistent with my results: ‘. .. Since physicians
`still write, by and large, brand name prescriptions for the pioneering
`(13> Blackwell Publishers Ltd. 2060.
`
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`352
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`ANDREA COSCELLI
`
`brand, unless an element of price competition is introduced at the level of
`the pharmacist the pioneering brand wilt continue to dominate the market
`E.
`.
`.1. 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 Iate entrants capturing
`market share’.
`
`Pharmaceuticai markets are subdivided into therapeutic classes. Foliow—
`ing most of the recent economic iiterature on pharmaceuticals (e.g., Stern
`[19959, 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-nicer drugs), and a sub-market as a specified molecule {for
`example,
`raniridme). 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 inciudes all the moiecuies
`which can theoretically be prescribed for a certain diagnosis. The
`moiecuies themselves differ according to side effects,
`interactions with
`other drugs, specific indications and prices. In the markets I study, a
`physician typicaiiy decides the appropriate molecule for the diagnosis and
`then she decides which trade—name1 version of the molecule to prescribe to
`the patient.
`My work focuses on a particular therapeutic market: anti—ulcer drugs
`(AOZB).
`I analyze this market because it accounts for a considerable
`proportion of worldwide expenditure on pharinaceuticais (around 5%,
`IMS International [1996]). Ulcers also required repeated treatment in the
`early 19905,2 a key feature of my analysis.
`I analyze six molecule
`submarkets (famotidme, ranitz‘dine, nizatidine, roxntidine, omeprazole and
`misoprosrole), which represent more than 90% of the prescriptions during
`the sample period (1990-1992). I restrict my sample to these six molecules
`because the other molecules represent more ‘mature’ and smaiier sub-
`markets, where some of the prices for identical brands differ.3 in each sub—
`market there is a patent-holder 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 ‘ditier’. in my analysis I
`focus on competing drugs based on the same active ingredient and
`
`lAll the drugs sold under a license or a patent in the Italian market have a trade name.
`2It has recently been found that approximateiy 80% of peptic ulcers can be cured by
`eradicating Helicobapter Pylori, a bacterium responsible for the recurrence of ulcers, by using
`a combination of antibiotics and antiwulcer drugs (Graham {1993;}.
`3Producers 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-
`markets are very small firms for whom the assumption of identical quality might not hold.
`© Blackwell Publishers Ltd, 2000.
`
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`rasranancas as THE PRESCRIPTION DECISION
`
`353
`
`marketed by important producers entering the market at
`-
`4
`time.
`
`the same
`
`If].
`
`THE DATA
`
`The main dataset (provided by the Istz'tato Superiore delta Smite ’) records,
`for a 10% sample of the population of the Metropolitan Area of Rome
`aged 15-65, all
`the prescriptions in the anti-ulcer (A028) drug market
`during the period 19904992. 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 310,000 observations. An obser—
`vation records the identity of the prescribing doctor, the identity of the
`patient, the year and month, and the particular presentation form of the
`drug prescribed (for example,
`1 package of ZANTAC 20 tablets, 150 mg
`each). An observation indicates exactly the drug bought by the patient,
`because the records are collected from pharmacies. In the patient—level
`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 all the prescriptions that 350 of these doctors wrote for any of
`their patients during the same period. The supplementary doctor~based
`dataset contains over 7i0,000 prescriptions and each observation records
`exactiy the same information as the patient—level dataset. The final dataset
`used in my estimations has more than 75,000 observations; it retains aii
`the observations in the patientnlevel dataset for the patients who received
`at
`least one prescription from one of the 350 doctors whose entire
`prescription history is known.
`
`Italian Market Three important characteristics of the Italian pharmav
`centical industry are: (i) there is no price and third—party payer variation,
`(ii) the over-thocounter (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 powor
`to subztitute 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 efl‘ectively controlled for all ‘objective’ dimensions of
`difi'erentiation between drugs; therefore I can proceed with my tests of doctor or patient
`indifference fairly confident
`that
`I have controlied for a large share of drug-specific
`heterogeneity.
`5 It is currently illegal throughout the EU and wiii remain so for several years even though
`the subject is now occasionaliy raised.
`5 Helicrstein‘s dataset, therefore, potentiality contains a large amount of measurement error
`in the prescription variable for states where substitution with generics is mandatory.
`© Biackwell Publishers Ltd. 2000.
`
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`354
`
`ANDREA COSCELL]
`
`for example, 98.6% of the iii-sample doctors prescribed each of the three
`available brands of omeprazole at least once during the sample period.
`Moreover, doctors usuaily prescribe multiple brands of each molecule in a
`given month. Thus doctors do not specialize in particular drugs over time
`or at a particular point in time.
`Patients are not limited to a singie brand either. At a given point, 40%
`of them will have had experienced a shift to a new brand of the same
`molecule. Different doctors treat the same patient difierentiy. Although
`the proportion of switches in the overall
`sample is 4.5%, among
`observations where patients change doctors, the proportion rises to 9%.
`This latter incidence, however, is much lower than the 48% switch rate that
`we wouid 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 strikingiy, 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 4i out of 44) go back to the treatment they received in the previous
`period. These patterns demonstrate clearly that the probability of receiving
`a new treatment is significantly influenced by the doctor’s identity, and
`that doctors differ in their choice among therapeutically equivalent drugs
`for the same patient. Next, I present a formal econometric model which
`accommodates alt these aspects of behavionr.
`
`1V.
`
`EMPIRICAL ANALYSIS
`
`iV(i). Theory
`
`I analyze the problem facing a doctor trying to choose among brands of
`a certain molecule. The entire analysis is conditional on the choice of the
`molecule, which is driven by a more complicated set of factors (indications,
`patient’s general health, side effects, price, etc); most of these factors are
`unobservable to the econometrician. By restricting myself to the analysis
`of ‘homogeneous’ goods, I can hold 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 molecule. In particular, doctor i must
`decide whether to prescribe the brand the patient received in the previous
`period (hereafter o for ‘oid’ brand) or a new brand, which might generate
`
`7 i do not analyze whether the doctor prescribed a different presentation form, becaese all
`the prodacers 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? Blackwell Publishers Ltd. 2000.
`
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`PREFERENCES IN THE PRESCRIPTION DECISION
`
`355
`
`‘new’ brand). More
`(hereafter n for
`a higher utility to the doctor
`specifiCally, a choice problem where the decision maker decides either to
`stay on the diagonal of a transition probabilities model, or to move off-
`diagonal to any other brand, is collapsed into a simpier problem where the
`decision-maker is confronted with the binary choice of either to stay on
`the diagonal or to move off-diagonal. Since the analysis is conditional on
`the choice of the molecule, ‘new brand” does not inciude other molecules.
`
`IV(ii). Estimation Strategy
`
`The empirical model parameterizes the probability of switching brands as
`a function of patient and doctor attributes. I define as an ‘old’ brand, a, at
`time t, the brand consumed by the patient at time t~— 1. This means that
`1 define as a ‘new’ brand, any brand that differs from the ‘old’ one without
`taking into account whether the ‘new’ brand was previousiy prescribed.
`This means that my analysis focuses on ‘firsnorder’ 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 chronoiogical order,
`the pre-
`scriptions where the patient, molecule and month are the same are
`randomiy ordered. Finally, the prescriptions in the sampie 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 variable, I do not distinguish
`between a prescription of two packages and a prescription of one
`package.8
`1 use probit specifications to test the nuli 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 suffers from the problem of initiai conditions common to
`most dynamic panel data models.
`I observe a sampie of doctors and
`patients for three years, but
`I do not have any information on their
`behavior before the sampie period begins. Ideaity, I would observe the
`doctors’ behavior
`since
`they started practicing and the patients’
`prescriptions since they were first
`treated for nicer problems. This is
`impossible;
`therefore foilowing one of the suggestions put forth in the
`literature (Heckman [1981]),
`i assume that the prescription process starts
`anew if patients have not had a prescription for six months. Thus, I only
`
`3This implies a two-package prescription leads to the same degree of state-dependence as
`a one-package prescription.
`© Blackwell Pubiishers Ltd. 2000,
`
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`356
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`ANDREA coscsLtI
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`use data on patients where I am abte to observe whether for six months
`prior they did or did not receive nicer medications. This basicaliy means
`that when the process restarts,
`the decision makers do not retain the
`information on anything which happened before.9
`
`IV(iii). Defieirion of the Variables
`
`Tables I and 11 list all the variables used in the estimations. SWITCH is
`the dependent variable and it takes vaiue 1 if patient j receives a brand at
`time t different from what he received at time t —_
`i (for the same molecule),
`it is 0 otherwise. We review those variabies that are not self~expianatory.
`
`Patients’ Variables
`Timeuinvariam #PRESCRIPTIONS distinguishes among patients ac»
`cording to the seriousness of their nicer problem (e.g., chronic versus
`
`Taste 1
`PAHENTwLEVEL VARanEs USED IN THE ESTIMATION
`
`Dependent variable
`SWITCH
`
`Patients variables
`GENDER
`AGE
`
`#PRESCRIPTIONS
`
`#DOCTORS
`
`#MOLECULES
`
`#SPELL—MOLECULE
`#PAST SWITCH ES
`#MONTHS
`
`Takes value 2 if the brand prescribed is difi‘erent from the
`brand previously prescribed, 0 otherwise
`
`Takes value 1 if female and 2 if maie
`
`Patient’s age
`Total number of prescriptions that the patient receives in
`the sample
`Total number of different physicians who prescribed at
`least one drag to the patient
`'i‘otal number of different molecules that the patient
`consumes in the sampie
`Number of prescriptions of the molecule up to time:
`Number of (within~mo§ecuie} switches up to time t
`Actuai number of months elapsed between the prescription
`at time t and the one at t —— l
`
`NEWDOCTORMTEMP
`
`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—RBT
`Dummy equal to 1 if the patient returns to a previous _
`physician, 0 otherwise
`
`9Most of the models were rerun with a Bumonth window instead. The results are not
`qualitatively difierent.
`© Biackwcl} Piibiishers Ltd. 2000.
`
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`PREFERENCES IN THE PRESCRIPTION DECISION
`TABLE II
`DOCTOR-LEVEL VARIABLES osso EN THE ESTIMATEON
`
`357
`
`Doctors' characterisri'cs—ami-ut‘cer market
`QUANTETY
`Average monthly quantity prescribed by the doctor in the entire
`market in the previous six months
`Average monthly hcrfindahl index across intends in the entire
`market in the previous six months
`Average monthly herfindahl index at the molecuie tevel in the
`entire market in the previous six months
`
`HERFBRAND
`
`HERFMOLE
`
`Doctors ’ characterisrics»moiecuie-specrfic
`MOLESHARE
`Share of prescriptions of the molecule by the doctor in the
`previous month
`Weighted proportion of the prescriptions of the molecule mitten
`for the old brand in the 2 previous months (East month’s share
`plus 0.9 of the previous month's share)
`
`% OLD BRAND
`
`occasional), white #MOLBCULES diflercntiatcs patients according to
`their wiilingness to change treatment. #DOCTORS controls for patient"
`specific preferences for changing doctor. Patients who change doctors
`more often probably gather more information on possible treatments;
`therefore it ought to be more difficult for a doctor to switch them; on the
`other hand, these patients might be more experimental.
`
`Time-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. #PAST SWITCHES increases only upon a withiremolecuie
`switch. These two ad~hoc variabies proxy for the switching tendency of
`a patient. For example, a patient who receives the tenth prescription of
`the molecule ranilidine, has #SPELL—MOLECULE: 10,
`if #PAST
`SWITCHES m0, this indicates that the patient has always been with the
`same brand. Finally, #MONTHS counts the actuai number of months
`elapsed between prescription episodes.
`it cannot exceed six, given the
`definition of a treatment episode. Finaiiy. there is a series of prescription—
`specyic dummies defining whether the patient is receiving the prescription
`from a substituting physician (NEWDOCTOR«TEMP),1° has permanently
`moved to a new physician (NEWDOCTOR-PERM), or is returning to
`the usual physician (NBWDOCTOR—RBT) after receiving a prescription
`from a substitute. These variables expiore whether doctors have different
`preferences for vendors of a particular molecule. If prescriptions decisions
`
`‘0 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 Publishers Ltd. 2000,
`
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`ANDREA COSCELLi
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`are determined solely by the patient’s condition, a change in the pre—
`scribing doctor should 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 immediately prior to the prescription episode.
`
`Constant across molecale markets Tabte 11 describes the variables in
`detail. QUANTITY distinguishes between heavy and light 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, whiie HERFMOLE controis for the dispersion in prescribing
`behavior of the doctor due to choice of diflerent molecules, which is
`dictated by heterogeneity in the patients’ pool. Its coefficient does not have
`any specific economic meaning:
`it simpiy allows me to interpret
`the
`coefficient on HERFBRAND as
`representing doctors’ preference for
`prescribing multiple brands, rather than their need to prescribe multiple
`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.
`
`Molecule-specific % OLD BRAND captures habit persistence by the
`doctor. MOLESHARE proxies for the importance that the molecule has
`for the doctor.
`
`Molecafe markets The number of competing products, the time since
`the moiecute entered the market, the size of the vendors, and the amount
`of advertising ail differ across molecule markets. I use molecule dummies
`to proxy for the difi‘erent competitive conditions in each molecnie market.
`Finally,
`I
`include monthiy dummies to controt for exogenous changes
`(such as reguiatory changes) that occur during the sample period.
`
`Summary Statistics Table III provides sampie summary statistics. There
`are slightiy more than 75,000 observations in the original sample, an
`observation being a prescription.
`I exciude the following observations:
`(i) patients who received prescriptions from doctors whose entire pre-
`scription history is not known, (ii) the first prescription of each meteorite
`for each patient, so as to initialize the process, and (iii) all prescriptions in
`the first 6 months and those more than 6 months apart for the same patient
`because of the initial conditions problem 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 slightly more prescriptions written to females in the sample,
`63 Blackwell Publishers Ltd. 2000.
`
`IMMUNOGEN 2249, pg. 10
`Phigenix v. Immunogen
`|PR2014—00676
`
`IMMUNOGEN 2249, pg. 10
`Phigenix v. Immunogen
`IPR2014-00676
`
`

`

`sssrenencns IN THE PRESCRIPTION DECISION
`TABLE III
`SUMMARY STATISTICS FOR THE SAME’LEW
`
`359
`
`Max
`Std. Dev. Min
`Mean
`
`
`Dependent variable
`Switch
`
`Patient 's variables
`Gender: i if female, 2 if male
`Age
`#In-sample prescriptions
`#Molecules prescribed in~sample
`#Diiferent doctors prescribing
`#Prescréptions of the molecule
`#Past switches up to time t
`#Months between prescriptions
`New doctor—temp
`New doctorwperm
`New doctorwret
`
`0.040
`
`0. t9?
`
`0
`
`1
`
`'
`
`1.484
`62.73
`29.478
`LS7
`1.629
`14.955
`0.396
`1.232
`0.018
`0.021
`0.024
`
`0.499
`12.65
`19.75
`$.09
`1.1 l
`12.68
`1.205
`1.277
`0.134
`0.143
`0.155
`
`l—F
`:5
`2
`l
`l
`2
`0
`0
`0
`0
`0
`
`ZWM
`85
`S26
`8
`14
`106
`E8
`6
`1
`1
`1
`
`Doctors’ skaracteristicsmanti»ulcer market
`Average monthly quantity prescribed in~sample
`Hcrfindahl brand level
`Herfindaiii molecule level
`
`Doctors ' characteristics-moiecule specific
`% Old brand prescribed
`Motecule share
`
`876.168
`0.243
`0.485
`
`297.568
`0.069
`0.113
`
`107.08
`0.09
`0.192
`
`2512.717
`0.68
`0.93
`
`0.97
`0.52%
`
`0.405
`0.256
`
`0.025
`0.025
`
`1.9
`1
`
`Molecuie sub-marker
`E
`0
`0.438
`0.742
`ranidine
`t
`0
`0.199
`0.04
`nizatr'a’z'ne
`1
`0
`0.09!
`0.008
`roxatidine
`
`omeprazoie 1 0.097 0.297 0
`
`
`
`
`whiic 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 particuiar motecule for the fourteenth time (#Si’ELLw
`MOLECULE) and who has already switched brands 0.4 times ($75“ PAST
`SWITCHES). The average time betWeen two prescriptions (#MONTHS)
`is less than two months. Finally, 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 temporariiy
`visits a doctor different
`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 (NEWDOCTOR—PERM). Finatiy, 2.4% of the
`prescriptions are written by the usual physician after the patient has been
`prescribed something by a 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
`© Biaekweli Publishers Ltd. 2000.
`
`IMMUNOGEN 2249. pg. 11
`Phigenix v. Immunogen
`|PR2014-00676
`
`IMMUNOGEN 2249, pg. 11
`Phigenix v. Immunogen
`IPR2014-00676
`
`

`

`360
`
`ANDREA COSCBLLI
`
`each molecule, the daily number of 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 mnitidine,
`followed by
`omepmzole with 9%.
`Table IV compares the market shares in the original sample to those in
`the estimation samples. First, column (1) shows that within-molecule
`market shares differ. Second, the main estimation sample is remarkably
`similar to the original sample, while the switching sample in column (3)
`
`TABLE IV
`MARKET SHARES IN THE SAMPLE
`
`Column (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 sample based on the total in-sample market shares
`Estimation
`Overall
`Estimation
`sample and
`Conditional
`
`sample
`sample
`switch
`probabilities
`
`Famotidl‘ne
`FAMODIL
`GASTRIDIN
`MOTIAX
`
`Ranitidz'ne
`RANInEN
`Rammoc
`RANinIL
`TRIGGER
`Uncax
`ULKOBRIN
`ZANTAC
`
`Nizatidine
`CRONIZAT
`Ntzax
`ZANEZAL
`
`Roxatidz‘ne
`GASTRALGIN
`NBOHZ
`ROXIT
`
`0.467
`0.448
`0.083
`
`0,02
`0.021
`0.419
`0.021
`0.029
`0.002
`0.483
`
`0.359
`0.581
`0.059
`
`0.332
`0.199
`0.468
`
`0.458
`0.453
`0.088
`
`0.03
`0.025
`0.432
`0.024
`0.024
`0.003
`0.467
`
`0.3