`Voiuine XLVIII
`September 2000
`
`0022- i 821
`No. 3
`
`THE IMPORTANCE OF DOCTORS’ AND PATIENTS’
`PREFERENCES IN THE PRESCRIPTION DECISION*
`
`ANDREA CoscsL:.i’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—ciependenoe 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—1evel can translate into
`sticicy and persistent market shares at the aggregate level.
`
`I.
`
`INTRODUCTION
`
`EN 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 therapeuticaliy 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
`*1 acknowledge financial support
`fellowship #1-3RBFMBlCT972232. I would like to thank the Istituto Superiore dz‘ Saniza’ for
`the use of their data. This paper was previously circulated under the titie ‘Are Market Shares
`in Drug Markets Affected by Doctors‘ and Patients‘ Preferences 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 usefui suggestions. All
`errors, however, are my own.
`1'Author‘s affiliation: National Economic Research Associates, E5 Stratford Place, London
`W] N 9AF, UK.
`email.‘ Andrea. Cosceiii@aera. com
`3 Blackwell ?ublishcrs Lid. 2000. :05 Cowley Road, Oxford OX4 HF. UK. and 130 Main Street. Malden. MA 02148. USA.
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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—narne
`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 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 arnount spent on direct—to—consurner prescription drug
`advertising rose from US$35rn in 1987 to US$357m in 1995, US$610tn in
`1996, and over US$l billion in 1997 (NERA [l999]). This spending choice
`reflects a widespread belief within the pharmaceutical
`industry that
`patients should have a role in the choice ofprescrzptfon 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 ltalian National Health
`Institute, which includes all the prescriptions in the anti—ulcer market from
`l990~l992 for a 10% random sample of the population of Rome aged
`iS-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 drfierent
`brands of the same molecule, as we would expect given their therapeutic
`equivalence. After reiecting the hypothesis, I attempt to isolate both the
`patient—level and the doctor—leve1 factors which are responsible for product
`differentiation.
`I focus specifically on the degree of tirnc-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 rnicro~
`
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`PREFERENCES lN THE PRESCRIPTION DECISION
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`35E
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`level can translate into sticky and persistent market shares at the aggregate
`level.
`
`The paper is organized as follows. In the next section l 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.
`
`n. Docrons’ DEMAND
`
`While the present study focuses on doctors’ demand for pharmaceutical
`products, most of the recent literature on pharmaceuticals (for example,
`Caves er al. [l99l}, Caves and Hurwitz {I988}, Berndt er ca‘. [1997], Scott
`Morton [1997, 2000}, and Scherer {l993]) has focused on supply-side issues
`(e.g., entry, pricing, advertising, R&D races). In his comment on Caves
`at al.
`[I991], Palces 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 i use
`allow me to separately identify doctor and patient effects.
`Much of the previous work on the demand for pharmaceuticals has used
`aggregate, n1arl<et—share data, which are much better suited to measuring
`the degree of difierentiation between various drugs rather than explain its
`causes. For example, Stern [1995]
`finds low substitutability between
`branded and generic drugs, while Ellison er at‘. [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 [I998]. 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 ctataset 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 molecule by a
`single doctor to many patients, and prescriptions of the same drug by
`many doctors.
`I987] analyzes competition between patent holders
`Gorecki U986,
`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
`analyses. Gorecl<i’s
`conclusions in [1986] are consistent with my results: ‘. .. Since physicians
`still write, by and large, brand name prescriptions for the pioneering
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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
`[. .
`.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 late entrants capturing
`market share’.
`
`i°harmaceuticai markets are subdivided into therapeutic classes. Follow-
`ing most of the recent economic Eiterature on pharmaceuticais (e.g., Stern
`[l99S}), 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,
`ranitidine). 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 molecules
`which can theoretically be prescribed for a certain diagnosis. The
`molecules themselves differ according to side effects,
`interactions with
`other drugs, specific indications and prices. In the markets I study, a
`physician typicaily decides the appropriate molecule for the diagnosis and
`then she decides which trademame’ version of the molecule to prescribe to
`the patient.
`My work focuses on a particular therapeutic market: anti—u1cer drugs
`(AOZB).
`I analyze this market because it accounts for a considerable
`proportion of worldwide expenditure on pharinaceuticais (around 5%,
`IMS International [I996]). Ulcers also required repeated treatment in the
`early 1990s} a key feature of my analysis.
`I analyze six molecule
`subrnaricets (famotidine, ranitidine, nizatidine, roxatidine, cmeprazole and
`misoprostole), 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-
`marlcets, where some of the prices for identical brands differ.3 In each sub-
`rnarlcet
`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 ‘differ’. In my analysis I
`focus on competing drugs based on the same active ingredient and
`
`‘All the drugs sold under a license or a patent in the Italian market have a trade name.
`alt has recently been found that approximateiy 80% of peptic ulcers can be cured by
`eradicating Helz’cobapterPy1ori, a bacterium responsible for the recurrence of ulcers, by using
`a combination of antibiotics and anti~ulcer drugs (Graham [I 993;}.
`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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`PREFERENCES EN THE PRESCRIPTION DECISION
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`marketed by important producers entering the market at
`time.4
`
`the same
`
`IE].
`
`THE DATA
`
`The main dataset (provided by the Istitato Saperfore della Sanita ’) records,
`for a 10% sainpie of the population of the Metropoiitan Area of Rome
`aged 15-85, 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 recorcis 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, 150mg
`each). An observation indicates exactiy the drug bought by the patient,
`because the records are collected from pharmacies. In the patienblevei
`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 suppiernentary doctonbased
`dataset contains over 710,000 prescriptions and each observation records
`exactiy the same information as the paticnt—lcvel dataset. The final dataset
`used in my estimations has more than 75,000 observations; it retains aii
`the observations in the patienblevel 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 pharrnav
`ceutical industry are: (i) there is no price and third-party payer variation,
`(ii) the over-the—countcr (OTC) market was tiny in the period of interest,
`and direct advertising to patients for prescription drugs had not yet
`started} and (iii) during the sample period, the pharmacist had no power
`to subgtitute 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 eflbctively controlled for all ‘objective’ dimensions of
`difierentiation 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 iliega] throughout the EU and wiii remain so for several years even though
`the subiect is now occasicnaliy raised.
`5 I-leliersteixrs dataset, therefore, potentiaiiy contains a large amount of measurement error
`in the prescription variable for states where substitution with generics is mandatory.
`© Biackwcli Publishers Ltd, 2000.
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`354
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`ANDREA COSCELLI
`
`for example, 98.6% of the in-sample doctors prescribed each of the three
`available brands of omeprazole at least once during the sample period.
`Moreover, doctors usually 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 single 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 difiercntly. 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 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 4E 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 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 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 econornetrician. 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 patier1t’s past
`prescription experience with this molecule. In particular, doctor i rnust
`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
`
`7 I do not analyze whether the doctor prescribed a different presentation form, because all
`the prodncers 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.
`its Blackwell Publishers Ltd. 2000.
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`PREFERENCES IN THE PRESCRIPTION DECISION
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`355
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`‘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 probabiiities model, or to move off-
`diagonal to any other brand, is collapsed into a sirnpier 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 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— I. 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 ‘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
`randorniy ordered. Finally, the prescriptions in the sarnpie are written
`either for one package or for two packages of the same brand. The sainpic
`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};
`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 econornetrician)
`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 ulcer problems. This is
`impossible;
`therefore foilowing one of the suggestions put forth in the
`literature (I-Ieckrnan [l981]), 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-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 coscettr
`
`use data on patients where I am abie 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
`
`IV/(iii). Definition of the Variables
`
`Tables I and II 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 I ~ 1 (for the same molecule),
`it is 0 otherwise. We review those variabies that are not selilexplanatory.
`
`Patients’ Variables
`Tz'me~—z‘nvariant #PRESCRIPTIONS distinguishes among patients ac»
`cording to the seriousness of their nicer problem (e.g., chronic versus
`
`'I‘A3Ll£ I
`PA'§‘IEN'§‘-LEVEL VAREABLES USED IN rm: ESTIMATION
`
`Dependent variable
`SWITCH
`
`Patients variables
`GENDER
`AGE
`
`#PRESCRIP'i’IONS
`
`#DOCTORS
`
`#MOLECUE.ES
`
`#SPELL—MOLECULE
`#“PAST SWITCH ES
`.#MONTHS
`
`Takes value 2 if the brand prescribed is diiiererzt from the
`brand previously prescribed, 0 otherwise
`
`Takes value 1 if female and 2 if maie
`
`Pa£ient’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
`Total number of different molecules that the patient
`consumes in the sarnpie
`Number of prescriptions of the molecule up to time 2
`Number of (within~mo§ecuEe} switches up to time t
`Actuai number of months elapsed between the prescription
`at time 5 and the one at I —— l
`
`NEWDOCTORJFEMP
`
`NEWDOCTOR—PERM
`
`Dummy equal to 1 if the physician is a temporary one For
`the patient, 0 otherwise
`‘Dummy equal to l if the physician is a new permanent one
`for the patient, 6 otherwise
`NEWDOCTOR—RET
`Dummy equal to 1 if the patient returns to a previous _
`physician, 0 otherwise
`
`93/Iost oi‘ the models were rerun with a 3«rrionth window instead. The results are not
`qualitatively difierent.
`© Biackwcli Ptibiishcrs Lid. 2900.
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`PREFERENCES IN THE PRESCRIPTION DECISION
`TABLE II
`DOCTOR-LEVEL VARIABLES USED its me ESTIMATEON
`
`357
`
`Doctors‘ characteristi'cs—an:i-ulcer market
`QUANTETY
`Average monthly quantity prescribed by the doctor in the entire
`market in the previoes six months
`Average monthly herfindahl index across hrands in the entire‘
`market in the previous six months
`Average monthly herfindahl inciex at the molecuie ievel in the
`entire market in the previous six months
`
`E-IERFBRAND
`
`HERFMOLE
`
`Doctors ’ characterisricswrnoiecnie-specgfic
`MOLESE-{ARE
`Share of prescriptions of the molecule by the doctor in the
`previous month
`Weighted proportion of the prescriptions of the molecule written
`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 differentiates patients according to
`their wiilingness to change treatment. #DOCTORS controls for patient-
`speczfic 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 tz'me—vary.r,‘n.g 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 withiwmolecnie
`switch. These two ad~hoc variabies proxy for the switching tendency of
`a patient. For cxarnpie, a patient who receives the tenth prescription of
`the molecule ranizidine, has #SPELL—MOLECULE= 10,
`it‘ #PAST
`SWITCHES m0, this indicates that the patient has always been with the
`same brand. Finally, #MONTHS counts the actual number of months
`elapsed between prescription episodes.
`it cannot exceed six, given the
`definition of a treatment episode. Finaiiy, there is 3. series of prescription-
`specific éurninies defining whether the patient is receiving the prescription
`from a substituting physician (NEWD0CTOR—'I“EMP),1° has permanently
`moved to a new physician (NEWDOCTOR—FERM), or is returning to
`the usual physician (NEWDOCTOR-RBT) after receiving a prescription
`from a substitute. These variables expiore whether doctors have diifcrent
`preferences for vendors of a particular molecule. If prescriptions decisions
`
`1° Most of the temporary substitutions occur when the physician is on vacation, or the
`patient is out of town when he needs a prescription.
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`ANDREA coscaLr.i
`
`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 molecule markets Table 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, while HERFMOLE controis for "the dispersion in prescribing
`behavior of the doctor due to choice of diflerem molecules, which is
`dictated by heterogeneity in the patients’ pool. Its coefiicient does not have
`any specific economic meaning:
`it simpiy allows me to interpret
`the
`coeiiicient on I-IERFBRAND 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.
`
`Molecule markets The number of competing products, the time since
`the moiecule 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 diiierent competitive conditions in each rnoleciiie market.
`Finally,
`I
`include monthiy dummies to controi 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 moleccie
`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 "-i~°/o of the observations.
`There are slightly more prescriptions written to females in the sample,
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`ranriskewcss IN THE PRESCRIPTION DECISION
`TABLE III
`SUMMARY STATISTICS FOR THE SAME-‘LE
`
`359
`
`Max
`Std. Dev. Min
`Mean
`
`
`Dependent variable
`Switch
`
`Patient ‘s variables
`Gender: 1 if female, 2 if male
`Age
`.#In-sample prescriptions
`#1V1o1ecules prescribed in~sample
`#Difi"erent doctors prescribing
`#Prescréptions of the molecule
`#Pastswitci1es up to time 1
`#-Months between prescriptions
`New doctor—ternp
`New doctorwperrn
`New doctor-rct
`
`0.040
`
`0. i9?
`
`0
`
`1
`
`'
`
`1.484
`62.73
`29.478
`%.87
`1.629
`14.955
`0.396
`1.232
`0.018
`0.021
`0.024
`
`0.499
`12.65
`19.75
`2.09
`1.1 1
`12.68
`1.205
`1.277
`0.134
`0.143
`0.155
`
`1-F
`E5
`2
`l
`1
`2
`0
`0
`0
`0
`0
`
`2»-M
`85
`S26
`8
`14
`106
`E8
`6
`1
`1
`1
`
`Doctors ’ cataracterisrics-arztiwalcer market
`Average monthly quantity prescribed in~sample
`E-ierfindahl brand level
`Herfindaiti molecule level
`
`Doctors ' characterist1'cs-mo1ecule 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
`093
`
`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
`nizatidine
`1
`0
`0.091
`0.008
`roxatidine
`
`omeprazoie 1 0.097 0.297 0
`
`
`
`
`while the average age is around 62. Turning to the tirnewarying covariates
`for the patients: the average prescription is written to a patient who is
`purchasing the particutar mokecute for the fourteenth time (#S2’ELL«
`MOLECULE) and who has already switched brands 0.4 times (#79 PAST
`SWITCHES). The average time between two prescriptions (#MONTHS)
`ts 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 temporarily
`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 3. new doctor, who then becomes the usual
`doctor for the patient (NEWDOC"I”OR—PERM). Finatly, 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
`© Btaekwelt Publishers Ltd. 2000.
`
`Page 11 0f21
`
`Page 11 of 21
`
`
`
`360
`
`ANDREA coscerm
`
`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
`omeprazole with 9%.
`Table IV compares the market shares in the original sample to those in
`the estimation samples. First, column (1) shows that withirvmolecule
`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 seed in
`column (2), corlclitional 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
`
`Famotidine
`FAMODIL
`GASTRIDIN
`MOTIAX
`
`Ranitidine
`RANIEJEN
`RANIBLOC
`Rameu.
`TRIGGER
`Uacax
`ULKOBRIN
`ZANTAC
`
`Nizatidine
`CRONIZAT
`NIZAX
`ZANEZAL
`
`Roxatidine
`GASTRALGIN
`Nsollz
`Roxn"
`
`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
`
`0.658
`0.453
`0.088
`
`0.03
`0.025
`0.432
`0.02%
`0.024
`0.003
`0.467
`
`0.371
`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
`0325
`
`0.285
`0.428
`0.286
`
`0.437
`0.454
`0.108
`
`0.031
`0.026
`0.435
`0.0%?’
`0.026
`0.007
`0.454
`
`0.453
`0.466
`0.08
`
`0.3
`0.272
`0.427
`
`Omeprazoie
`0.344
`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 basecl on the data set described in Section Ill.
`© Blackwell Publishers Ltd. 2000.
`
`Page 12 of 21
`
`Page 12 of 21
`
`
`
`PREFERENCES IN THE PRESCRIPTION DECISION
`' Taste V
`CORRELATION TABLE
`
`301
`
`
`
`
`
`
`SWITCH #SE’MO1. .#l‘\/EONVariable #PASTSW °/oOLIDBR I-IERFB
`SWITCH
`1.000
`1.00
`#SPELL—MOLECULE
`—-0.05
`1.00
`#'MON’I”HS
`0.0587 —0.02332
`i .00
`-0.071?’
`#PAST SWITCHES
`0.1897
`0.2442
`1.00
`—0.09S9
`——0.36l
`#OLD BRAND
`—