MANAGEMENT SCIENCE
`Vol. 50, No. 12, December 2004, pp. 1704-1715
`IssN0025—1909 I1’-:TssN1526—5501 |04|5012|1704
`
`int31IfiE®
`D0110.1287/mnsc.1040.0281
`© 2004 INFORMS
`
`Are Physicians ”Easy Marks”? Quantifying the
`Effects of Detailing and Sampling on
`New Prescriptions
`
`Natalie Mizik
`Graduate School of Business, Columbia University, 3022 Broadway, New York, New York 10027-6902, nm2079@columbia.edu
`
`Robert Jacobson
`Department of Marketing, School of Business, University of I/Vashington, Seattle, I/Vashington 98195-3200,
`yusho@u.washington.edu
`
`Much public attention and considerable controversy surround pharmaceutical marketing practices and their
`impact on physicians. However, views on the matter have largely been shaped by anecdotal evidence or
`results from analyses with insufficient controls. Making use of a dynamic fixed-effects distributed lag regres-
`sion model, we empirically assess the role that two central components of pharmaceutical marketing practices
`(namely, detailing and sampling) have on physician prescribing behavior. Key differentiating features of our
`model include its ability to (i) capture persistence in the prescribing process and decompose it into own-growth
`and competitive-stealing effects, (ii) estimate an unrestricted decay structure of the promotional effects over
`time, and (iii) control for physician-specific effects that, if not taken into account, induce biased coefficient esti-
`mates of detailing and sampling effects. Based on pooled time series cross—sectional data involving three drugs,
`24 monthly observations, and 74,075 individual physicians (more than 2 million observations in total), we find
`that detailing and free drug samples have positive and statistically significant effects on the number of new
`prescriptions issued by a physician. However, we find that the magnitudes of the effects are modest.
`
`Key words: pharmaceutical marketing; salesforce effectiveness
`History: Accepted by Linda Green, public sector applications; received March 8, 2004. This paper was with the
`authors 1 month for 1 revision.
`
`Introduction
`
`As the cost of prescription drugs continues to escalate,
`increased public attention is being focused on the
`marketing practices of the pharmaceutical firms as
`one source of the problem. Direct—t0-physician activ-
`ities account for the bulk of U.S. pharmaceutical
`firm promotional spending. IMS Health (2003) esti-
`mates that over $5.8 billion was spent in 2002 on
`detailing,
`i.e., pharmaceutical sales representatives
`(PSRS) visiting physicians to promote their firm’s
`drugs. In addition, the retail value of the free drug
`samples distributed during these visits is estimated
`at $11.5 billion.
`
`A detailing visit typically lasts two to five minutes
`during which time a PSR discusses one to three of
`the company’s drugs. Information (and, at times, mis-
`information) about a drug's composition, therapeutic
`value, proper dosage, and potential side effects is
`communicated (Zigler et al. 1995). Often, PS-Rs will
`also dispense samples and possibly offer small gifts
`to the physician. At issue is whether these interac-
`tions with PSRs compromise physician integrity and
`affect their prescribing behavior. More precisely, the
`key public policy issue is the extent to which the
`
`industry's promotional tactics lead to an increase in
`appropriate versus inappropriate use of drugs in a
`cost-effective manner.
`
`Concern that pharmaceutical marketing practices
`have exacerbated increases in public health costs has
`prompted government actions at the federal and state
`levels. For example, in 2002 the federal government
`issued a warning to the drug industry to curtail some
`of their marketing practices (Washington Post 2002).
`HR. 2356, which calls for ongoing annual funding of
`$75 million to conduct comparative cost-effectiveness
`drug studies, was introduced in Congress in ]une
`2003. A primary intent of this legislation is to pro-
`vide objective scientific evidence to "reduce doctors’
`reliance on marketing information from the pharma-
`ceutical industry" (Pear 2003). Given the fact that one
`of every five dollars spent on pharmaceutical drugs in
`the United States is paid for by a state program, state
`governments have also taken steps to counter PSR
`influence. Most notably, several states have under-
`taken counterdetailing initiatives (Gold 2001). State
`employees visit physicians in hopes of persuading
`them to switch from prescribing branded drugs to
`prescribing lower-cost generic drugs.
`
`Depomed Exhibit 2126
`
`

`
`Mizik and Iacobson: Quantifyiiig the Effects uf Detailing and Sanzpling on New Prescriptiun.s
`Management Science 50(12), pp. 1704-1715, ©2004 INFORMS
`
`1705
`
`Prescription drug expenditures are projected to
`remain the fastest—growing sector of health care
`expenditures. They are expected to account for 14.5%
`of $3.1 trillion health care expenditures by 2012
`(compared with approximately 10% in 2001). With
`recent legislation providing a Medicare drug benefit
`expected to cost the federal government $534 billion
`over the next decade, it is no wonder that the impact
`of pharmaceutical industry marketing practices is of
`keen interest to policymakers, the business commu-
`nity, and the general public.
`Two competing views have dominated discussion
`on the matter. The prevailing view contends that
`PSRs significantly influence physicians’ prescribing
`behavior and that this influence has negative effect
`on patients’ welfare,
`in that PSRs encourage physi-
`cians to prescribe more expensive branded drugs.
`Many public policy organizations and consumer
`advocacy groups adhere to this View (see, for exam-
`ple, www.nofreelunch.org). The prominent alternative
`view argues that PSRs do influence physicians’ pre-
`scribing behavior, but that this influence is positive
`in that PS-Rs provide physicians with valuable infor-
`mation. As a result, physicians are better informed
`and make better choices for their patients. Pharma-
`ceutical companies and industry groups advocate this
`second view.
`
`Despite the substantial resources that pharmaceu-
`tical companies invest in promoting their products
`and the controversy associated with pharmaceutical
`marketing practices, surprisingly little is known about
`the magnitude of the impact that PSR Visits and free
`drug samples have on physician prescribing behavior.
`Narayanan et al. (2003) report a pharmaceutical exec-
`utive as stating, ”No one is really sure if sending the
`legions of reps to doctors’ offices really works. Every-
`one is afraid to stop it, because they don't know what
`difference it's making” (p. 4).
`In point of fact, much of the evidence on PSR
`effectiveness
`is anecdotal. The empirical
`studies
`investigating the issue have been subject to data or
`methodological limitations that restricted their ability
`to control for potential biases and have come to
`contradictory conclusions regarding even the central
`issues: the effects of detailing on prescriptions (e.g.,
`Parsons and Abeele 1981 versus Gonul et al. 2001),
`of detailing on price elasticity (e.g., Rizzo 1999 Versus
`Gonul et al. 2001), and even of price on sales (e.g.,
`Rizzo 1999 versus Gonul et al. 2001).
`We have obtained access to a unique database
`that allows us to undertake econometric analysis
`that overcomes a number of fundamental limitations
`
`existing in past research. In particular, making use
`of a dynamic fixed-effects distributed lag model that
`accounts for physician-specific effects likely to induce
`
`bias if left uncontrolled, we assess the effect of detail-
`
`ing and sampling on physician prescribing behavior.
`The large number of observations in the database (it
`involves a total of more than 2 million observations)
`allows us to accurately pinpoint the impact that inter-
`actions with PSRS have on the number of new pre-
`scriptions issued by physicians.
`We find that, although detailing and free drug sam-
`ples have a positive and statistically significant asso-
`ciation with the number of new prescriptions issued
`by a physician, the magnitudes of the effects are mod-
`est. As such, our results challenge the two domi-
`nant views and support the contention that, rather
`than being easy marks, physicians are tough sells.
`This realization is important because the public policy
`debate continues over how best to address the high
`cost of prescription drugs.
`
`PSR Influence on Physicians
`Most discussions of PSRs have focused on the factors
`
`facilitating their influence. Unquestionably, PSRS pro-
`vide physicians with information about new drugs,
`new indications, dosages, and interactions for existing
`medicines. Azoulay (2002) finds evidence that detail-
`ing diffuses product information. Avorn et al. (1982)
`report that 20% of surveyed physicians view informa-
`tion provided by PSRS as ”very important” in influ-
`encing their prescribing behavior. Furthermore, PSRs
`are trained in persuading physicians. Detailing takes
`the form of presenting facts and, as has been doc-
`umented (Zigler et al. 1995), misrepresenting facts
`about the drug in an effective manner. Finally, mere
`exposure or salience effects might lead to a temporary
`increase in prescribing following a PSR visit. Numer-
`ous studies have reported high physician responsive-
`ness to PSR activity attributed it to PSR persuasive-
`ness (Avorn et al. 1982, Powers 1998).
`Less attention has been paid to the factors limiting
`PSR effectiveness. The key consideration here is that
`PSRs are not the only or even the primary source
`of information about drugs for physicians. Scientific
`papers, advice from colleagues, and a physician's own
`training and experience also influence prescribing
`practices. Indeed, most physicians View these other
`influences as far more important than that of PSRs
`(Peay and Peay 1990).
`that many
`PSR influence is limited by the fact
`physicians have skeptical or negative attitudes toward
`PSRS (Lichstein et al. 1992, McKinley et al. 1990).
`Attribution theory suggests that with low source
`credibility, which is determined by factors such as
`a source's trustworthiness and expertise (Dholakia
`and Sternthal 1977), arguments in a message will
`be discounted (Eagley and Chaiken 1975). Physicians
`recognize that PSRS are neither experts nor com-
`
`

`
`1 706
`
`Mizik and Iacobson: Qm11'1t7_Tfying the Effects 0fDet411’l7Tng and Sampliiig on New Prescriptions
`Management Science 50(12), pp. 1704-1715, ©2004 INFORMS
`
`pletely trustworthy. They realize that information pre-
`sented is biased toward the promoted drug and is
`unlikely to be objective or even accurate (Connelly
`et al. 1990). Thus, physicians will discount informa-
`tion received from a PSR.
`
`Some additional characteristics of physicians would
`seem to make them particularly tough sells. Friestad
`and Wright's (1994) persuasion knowledge model
`suggests that targets of persuasion use their knowl-
`edge about the persuasion agent and can effectively
`cope with and even achieve their own goals during a
`persuasion attempt, e.g., obtaining free drug samples
`that can be later distributed to patients. Campbell and
`Kirmani’s (2000) tests of the persuasion knowledge
`model reveal that busy targets with accessible agent
`motivation (a profile that would fit most physicians)
`are particularly effective in resisting persuasion.
`When cast within the workings of other sources
`of influence, we would expect the ability of PSRS to
`influence physician behavior to be relatively small. As
`such, we hypothesize a relatively small effect of PSR
`activity on physician prescribing behavior.
`
`Previous Empirical Research
`The various studies assessing the effect of PSR activ-
`ity on physician prescribing behavior have generated
`conflicting results. Indeed, on some of the most cen-
`tral issues-—ranging from the effects of detailing on
`prescriptions, of detailing on price elasticity, and even
`of price on sales—studies have come to diametrically
`opposite conclusions. Data and methodological limi-
`tations, however, raise concerns about the inferences
`
`drawn from these analyses.
`A few quasi-experimental studies of the issue orig-
`inate in the medical community. These studies com-
`pare physicians who did not see PSRS or were visited
`less frequently by PSRs to physicians who saw PSRs
`or were visited more frequently by I’SRs (Chren and
`Landefeld 1994, Powers 1998). The limitation of these
`studies is that
`they are not randomized: PSRs do
`not determine which physicians to visit on a random
`basis. Rather, PSRS tend to see physicians who are
`more likely to utilize the drug or who prescribe in
`higher volume. This consideration invalidates these
`attempts to assess the effect of PSRs independent of
`controls accounting for motivation influencing PSR
`behavior.
`
`The ability to potentially control for other influ-
`ences is an advantage of regression—based analysis.
`Past research has made use of different regression
`techniques to assess PSR influence. Unfortunately,
`it has been inadequate in controlling for physician-
`specific effects. Parsons and Abeele (1981) use data
`for 24 months and 14 territories to model the number
`
`of prescriptions sold in a given territory for a given
`month as a function of sales calls. Interestingly, the
`
`estimated main effect of detailing was negative. The
`most dominant explanatory factor in the model
`is
`sales lagged one period, which would reflect per-
`sistence in behaviors and carryover effects magnify-
`ing the influence of detailing. Alternatively, lagged
`sales could be reflective of territory-specific effects
`that are not modeled and, as such, could lead to
`
`biased estimates. Wotruba (1982), for example, raises
`this possibility of territory—specific effects to question
`the reported effects of detailing.
`Rizzo (1999) uses annual data for the period 1988-
`1993 and for 46 drugs to estimate a brand-level model
`linking prescriptions for a drug for a given year
`to pharmaceutical company marketing activities. He
`finds that price is negatively related to sales and that
`detailing is anticompetitive in that it decreases price
`sensitivity. Detailing is also found to have a direct
`positive effect on sales. Surprisingly, no consideration
`is given to the dynamic properties of sales. The classic
`spurious regression characteristics, ie, very high R2
`in the presence of substantial unmodeled autocorre—
`lation, appear to be present. As such, questions exist
`about the validity of both the point estimates and
`standard errors reported in the analysis.
`Gonul et al. (2001) use data involving 1,785 patient
`visits to estimate a multinomial logit model assess-
`ing factors influencing physician prescribing behav-
`ior. Exactly opposite to the findings of Rizzo (1999),
`they report that price has a positive effect on prescrip-
`tion probabilities and that detailing increases price
`sensitivity. They find positive effects of detailing and
`sampling, but do not discuss the implications of their
`magnitudes. These magnitudes, calculated based on
`descriptive statistics, imply elasticities that are sur-
`prisingly large? The elasticity estimates for the seven
`drugs studied, evaluated at the mean level of detail-
`ing and sampling, average 41% for detailing and 48%
`for sampling. Particularly for samples, which have a
`negligible marginal cost, their estimated coefficients
`imply enormous returns to enhanced PSR activity. In
`point of fact, these substantial effects could arise, not
`from the influence of PSR activity, but rather as an
`outgrowth of a joint correlation with an omitted factor
`from the model, e.g., larger practices prescribe more
`and receive more free samples.
`(2001) explicitly
`A concern, which Gonul et al.
`acknowledge,
`is over the role of physician-specific
`effects that can induce a bias in the estimated coeffi-
`
`cients. They state (p. 84),
`
`prescription behavior patterns might be strongly influ-
`enced by factors other
`than the explanatory vari-
`ables we include in our model. Examples are physi-
`
`1 The elasticity of prescription probability Pf to covariate xjk in a
`conditional logit model is calculated as (HP,-/17/,-)/(ax/,-k/ac]-k) 2 Bk akxjk =r
`(1 — Pf).
`
`

`
`Mizik and Iacobson: Quantifyiiig the Effects uf Detailing and Sanzpling on New Prescriptions
`Management Science 50(12), pp. 1704-1715, ©2004 INFORMS
`
`1707
`
`. Ignor-
`.
`cians' unobservable personal characteristics. .
`ing these factors might bias the coefficients of the
`included explanatory variables.
`
`The extent to which their estimates are biased by
`the failure to control for unobservable factors remains
`
`unanswered, but this is one consideration that might
`account for the large estimated effects.
`
`Empirical Analysis
`A key benefit of utilizing pooled time series cross-
`sectional (panel) data is the ability to test for and
`control the effect of unobserved fixed factors. These
`
`unobserved factors, if left uncontrolled, can induce
`
`bias in the coefficient estimates of the explanatory fac-
`tors included in the model. Past research has either
`
`not used panel data or not made full use of the ben-
`efits of panel data analysis. We make use of pooled
`time series cross-section observations (24 months of
`observations across 74,075 physicians) and panel data
`statistical methods (i.e., a dynamic fixed—effects dis-
`tributed lag regression model) to assess the effect
`of detailing and sampling on physician prescribing
`behavior.
`
`Data
`
`Access to the data was gained from a US. pharmaceu-
`tical manufacturer with the only condition of ensur-
`ing the anonymity of the firm and the drugs in the
`study. Two different sets of data were merged to form
`the database. One data set pertains to the number
`of new prescriptions for the studied drugs and their
`competitors issued by physicians during a month.
`The new prescription measure reflects both new and
`repeat usage, but does not reflect refills accompany-
`ing the prescriptions. These data cover a 24-month
`period for three widely prescribed drugs. The second
`data set pertains to detailing and sampling activity
`by PSRs for the same three drugs. The two data sets
`were merged into one database containing prescrib-
`ing and promotional activity information by month
`and physician.
`To reduce the possible influence of extreme val-
`ues (outliers) that would arise from, for example,
`data entry errors and the common practice of one
`physician signing for all samples that later get dis-
`tributed to a group of physicians attending a confer-
`ence, we excluded the top 0.5% of observations for the
`number of details, samples, and new prescriptions.
`We later undertook sensitivity analysis on alternative
`definitions of outliers (e.g., 0%, 1%, 5%) and found
`results in close correspondence across these alterna-
`tive samples.
`Table 1 presents basic background information and
`descriptive statistics for the drugs included in our
`study. The drugs differ on a variety of dimensions:
`They have been on the market from less than 1 year
`
`to 11 years; annual sales range from under $0.5 billion
`to more than $1 billion; they come from different ther-
`apeutic areas. Although the effect of detailing can
`vary across drugs, analysis of these three drugs offers
`some generalizable insights, not only because they
`provide a cross-section of drugs in the marketplace,
`but because they represent more than 4 million PSR
`interactions with physicians.
`
`Model
`
`We employ the following dynamic fixed-effects dis-
`tributed lag regression model to assess the effect of
`detailing and sampling on new prescriptions:
`
`Prescribe,., 6
`
`6
`
`: oz, + 2 B]- >i< Details,-,,j -l- 2 ‘y/.; >:< StI11Z}7l€SI.t_].
`j=O
`,«'=o
`6
`6
`
`+ Z A, Competitormj -1- Z Cb]: >x< Prescrz'be,,,_]-
`j=0
`_7'=1
`T
`11
`
`- Z 87 * Tz'me(r) +
`-—l
`
`s21
`
`>‘s Specz'alty(s) >k Trend,
`
`“ 3:’: r
`
`(1)
`
`where Prescr1'l76,,, Detaz'ls,,, Samples,.,, and Competiton,
`are, respectively,
`the number of new prescriptions
`issued, the number of PSR visits, the number of free
`
`drug samples received, and the number of new pre-
`scriptions issued for competitive drugs by physician i
`at time period t. Tz'me(r) is an indicator function that
`takes on the value 0 prior to the time period 1' and 1
`from the time period 7' on, SpeciLzlty(s)
`is an indi-
`cator function that takes on the value 1 when the
`
`specialty area of the physician is s, 0 otherwise (i .e.,
`separate dummy variables for each of the 11 spe-
`cialty areas), and Trend is the observation number
`for a given month and year. Because it includes both
`current—term and lagged Variables in the model, Equa-
`tion (1) allows for a wide range of possible effects and
`influences, e.g., serial correlation (current—effects) and
`state-dependent (persisting) dynamic relationships.
`A key characteristic of Equation
`is that it allows
`for a physician-specific effect, ie, the intercept ct,
`is allowed to vary by physician. This consideration
`acknowledges that physician behavior patterns are
`influenced by unobserved or unobservable factors,
`e.g., physician characteristics. To the extent that these
`unobserved factors are correlated with detailing and
`sampling, analysis not controlling for their effects will
`result in biased estimated effects for the marketing
`phenomena. Although a Hausman (1978) specification
`test can empirically assess the role played by fixed
`effects, we have a priori reason to believe that these
`unobserved factors will in fact be correlated with mar-
`
`keting activity. For instance, larger practices will gen-
`erate more prescriptions and will also attract more
`
`

`
`Mizik and Iacobson: Qm11'1t7_Tfying the Effects ofDet41il7Tng and Samplirtg on New Prescriptions
`Management Science 50(12), pp. 1704-1715, ©2004 INFORMS
`
`Table1
`
`Drug Profiles
`
`Sales range (US$)
`Time on the market at the
`beginning of the study period
`Estimated number of competitors
`in the respective therapeutic area
`Mean number of details per
`physician per month
`Mean number offree drug samples
`per physician per month
`Mean number of monthly new
`prescriptions per physician
`Average number of refills following
`one new prescription
`Recommended duration of therapy
`
`Cost of therapy relative to other
`drugs in the therapeutic area
`Mean number of monthly new
`prescriptions per physician in
`the respective therapeutic area
`Therapeutic area is
`Specialty area of top prescribers
`Number of physicians
`Number of data points
`
`Drug A
`
`0.5 to 1 billion
`
`3 years
`
`12
`
`1.73
`(1.75)
`4.34
`(9.76)
`13.18
`(14.81)
`2-3
`
`Drug B
`
`over 1 billion
`
`11 years
`
`Drug C
`
`under 0.5 billion
`6 months
`
`18
`
`1.98
`(1.70)
`7.79
`(13.71)
`8.82
`(10.43)
`2-3
`
`11
`
`1.73
`(1.44)
`4.02
`(7.73)
`2.27
`(3.58)
`2-3
`
`3 months, and as maintenance with
`periodic patient reexamination
`Ave rage
`
`42.91
`(43.63)
`
`Relatively new
`Psychiatry
`10,516
`252,384
`
`3 months and up to 9 months
`
`Not yet established
`
`Above average
`
`48.80
`(53.73)
`
`Well established
`Primary carett
`55,896
`1,341,504
`
`Ave rage
`
`22.46
`(19.03)
`
`Well established
`Primary carett
`30,005
`720,120
`
`Illote. Standard errors are in parentheses. The sum of the number of physicians in each of the three data sets is greater than the total
`number of physicians in the study (74,075) because some physicians are in the upper 60 prescribing percentile for more than one of
`the drugs in ourstudy.
`“The primary care specialty area includes family practice, general practice, internal medicine, and osteopathy.
`
`detailing. As such, a spurious positive correlation, i.e.,
`unrelated to any potential effects of detailing, will
`exist between detailing and prescriptions due to a
`joint correlation with practice size.
`Because the effects of PSR activity are unlikely to
`be limited to the month when the visit occurred, we
`
`allow for current and lagged effects for both detailing
`and sampling. The number of observations available
`in our data sample allows us to directly estimate sep-
`arate effects for lagged terms. As such, we do not
`need to impose a specific decay pattern as a neces-
`sity for preserving degrees of freedom. We do, how-
`ever, need to specify the length of time that a PSR
`visit might influence physician behavior. We select a
`six—months lag length under the view that the effect
`of a visit will dissipate substantially over this period,
`but we also test for longer-term lagged effects. Still,
`we expect the effects of detailing to be largest in the
`months directly following the visit. The cumulative
`direct effect of detailing and sampling can be obtained
`simply by summing the coefficients, i.e., 216.208) and
`Z1620 7'1"
`Lagged values for new prescriptions (Presc’ril7e,.,,,,]-)
`capture autocorrelation in the series
`that arises
`through inertia and persistence in physician behavior.
`These autoregressive effects play a key role in that
`they magnify the effects of detailing and sampling.
`That is, the total effects of detailing and sampling
`
`involve both a direct effect on prescriptions and an
`indirect effect that arises through persistence in physi-
`cian behavior. The total effect of detailing and sam-
`pling can be calculated as
`
`§r9,r/l1f;::l<r,rl
`
`iv./[1-§{:,r.],
`
`r=o
`
`respectively.
`We include both lagged own prescriptions and
`lagged competitors’ prescriptions,
`thus the model
`separates lagged total demand dynamics into two
`key components: competitive substitution and own
`demand growth. Lagged own prescriptions will have
`a positive effect on prescriptions. Lagged competitors’
`prescriptions will have a negative impact on prescrip-
`tions because they capture the substitution effects
`that physicians make between competing drugs. The
`current competitive prescriptions, however, will cap-
`ture two different phenomena. They will reflect not
`only substitution effects, but also changes in total
`demand due to market expansion or contraction. In
`this regard, current-term competitive effects will act
`as a proxy for two different phenomena with oppo-
`site effects, i.e., negative substitution effects and posi-
`tive market demand effects. As such, the current-term
`
`coefficient (A0) will depend on the relative magnitude
`of the two conflicting effects and, therefore, the sign
`of the effect cannot be postulated a priori.
`
`

`
`Mizik and Iacobson: Quantzfyiiig the Effects uf Detailing and Sanzpling on New Prescriptiun.s
`Management Science 50(12), pp. 1704-1715, ©2004 INFORMS
`
`1709
`
`The other variables in Equation (1) are time-period-
`specific indicators and specialt_y—specific trends. The
`time-period-specific indicators (the coefficients 8,)
`allow for the fact that the number of prescriptions
`can shift across time periods. These intercepts capture
`not only seasonal effects but all brandwide influences
`that shift prescribing behavior across all physicians
`(e.g., price changes, changes in the set of alternative
`medications available, changes in advertising cam-
`paigns, etc.). That is, the inclusion of the time-specific
`indicator variables will capture all effects common
`across physicians, which would include the diffu-
`sion pattern for the drug, research reports in scien-
`tific journals, any negative or positive publicity for
`the brand or its competitors, etc. The 11 specialty-
`specific trends KS capture influences that shift pre-
`scribing behavior across all physicians in a particular
`specialty. After we take first differences,
`the time-
`period-specific indicators and the specialty—specific
`trends are transformed into time-period-specific inter-
`cepts and specialty-specific intercepts.
`To remove the influence of physician specific effects
`(i.e., a,.), we take first differences of Equation (1) to
`obtain
`
`APrescrz'l7e,,
`6
`
`6
`
`= Z fly. >t< ADetails,-,,J,- + 2 'y,- ASa1n;9lesl.t_7.
`j=0
`7:0
`6
`6
`
`-
`
`A) >l< AC0nzpetit01‘,,_", +
`11
`
`i=0
`T
`
`i=1
`
`(,l)]- >l< AP1'escrilJe,,___j.-
`
`- Z 6, >t< ATz'me(r) + 2 K5 >t< Specz'alty(s) + 77,-,.
`r=l
`s=1
`
`(2)
`
`Of notable absence from our model, due to lack of
`
`available data, is competitive marketing effort. How-
`ever, several considerations suggest that the poten-
`tial bias in the estimated effects of firm detailing and
`sampling stemming from this omission will be minor.
`First, the inclusion of competitor sales will capture
`some of the effects of competitive marketing activi-
`ties and thus reduce potential omitted variable bias.
`in essence, the competitor sales variable in the model
`will act as a proxy variable (Wickens 1972) for com-
`petitor marketing expenditures and, as such, reduce
`potential omitted variable bias. As a result, we expect
`the correlation between the change in firm detailing
`and the change in competitor detailing for a given
`physician (after controlling for changes in competitor
`sales) to be minor, which would lead to minimal omit-
`ted variable bias. Pharmaceutical firms do not coordi-
`
`nate detailing activity with competitors, nor do they
`have access to information about competitive detail-
`ing at the individual physician level. To get some per-
`spective as to the magnitude of correlation between
`firm and competitive detailing, we undertook a sepa-
`rate analysis based on changes in monthly brand-level
`
`data for antiulcer drugs? We found that, after making
`use of competitive sales as a proxy variable, monthly
`changes in brand detailing exhibited little correlation
`(0.01) with monthly changes in competitor detailing.
`Given the absence of correlation at the brand level, the
`
`correlation at the physician level can also be expected
`to be similarly small and, indeed, even smaller. As
`such, the magnitude of this bias in the estimated effect
`of detailing caused by the exclusion of competitive
`detailing from the analysis can be expected to be min-
`imal, or even completely absent.
`We estimate Equation (2) using instrumental vari-
`able estimation as ordinary least squares will generate
`biased estimates of the coefficients for APrescril7e.*,.,_1
`and ACOmpetit0rl.l. By construction, APrescrz'l7e,.,i1 will
`be correlated with the differenced error
`term 17,,
`and, just as substitution effects cause competitor pre-
`scriptions to influence own prescriptions, own pre-
`scriptions will influence the amount of competitor
`prescriptions. Following Anderson and Hsiao (1982),
`we use lagged values of the levels of the series
`(values at time period t— 2 and earlier) to gener-
`ate instrumental variable estimates for APrescril7e,-,_1
`and ACOmpetz'tor,,. This procedure generates consis-
`tent (i.e., asymptotically unbiased) estimates of the
`parameters and their standard errors?
`
`Results
`
`For each of the three drugs in our study, we estimated
`the Equation (2) regression model. Table 2 reports
`the estimated coefficients. Figures 1 and 2 graphically
`depict the estimated direct effects of detailing and
`sampling, respectively.
`
`Persistence in Prescribing Behavior. For each of
`the three drugs in the study we observe signifi-
`cant persistence in physicians’ prescribing behavior.
`Although the first-order autocorrelation is the most
`substantial for all three drugs, significant higher—order
`effects are present as well. For Drug A the estimated
`coefficients for months 1 through 6 of 0.357, 0.205,
`
`2 We thank Ernst R. Berndt for granting us access to these data. See
`Berndt et al. (2003) for a complete description of the data. Consis-
`tent with lack of correlation between firm and competitor detailing,
`we also found little difference in the estimated effect of detailing
`on sales generated from a model that included competitive detail-
`ing versus a model that excluded competitive detailing (ie, the
`difference was less than 3%).
`3The consistency of the lagged-variable-based instrument hinges
`on the assumption that the disturbances are serially uncorrelated;
`see, for example, Arellano and Bond (1991) for a discussion. VVe
`included higher-order lagged endogenous variables in the model
`to ensure this would be the case and we find no evidence to the
`
`contrary. For example, for Drug A the second-order autocorrela-
`tion of the residual is -0.002‘), with a t-stat of -0.96; the 3rd order
`autocorrelation is 0.00035, with a t-statistic of 0.12. Indeed, our esti-
`mated residual correlation matrix is in near exact correspondence
`with that postulated by a fixed effects, autoregressive model for
`each of our three drugs (Arellano 2003).
`
`

`
`Mizik and Iacobson: Qm11'1t7_Tfying the Effects 0fDet41il7Tng and Sampliiig on New Prescriptions
`Management Science 50(12), pp. 1704-1715, ©2004 INFORMS
`
`Table 2
`
`The Eflecls of PSR Detailing and Drug Samples on Physician New Prescriplion lssuings: Equation (2)
`Estimates
`
`Dependent variable: APresoribe,-,.
`
`Drug A
`
`Drug B
`
`ADeIai/5,,
`ADeta/'/s,-,-_1
`ADetai/s,-F2
`ADetai/s,-,__3
`ADeta/'/s,—,,_,
`ADeta/'/s,,,5
`ADetai/s,-H5
`Asamples,-,
`ASampIes,,,1
`Asamp/es,,,,
`A8an7p/es,,,_ 3
`A Samples,,_ 4
`Asamples,-H3
`Asamp/es,-M5
`
`APresoribef,i1
`APresoribe,-F2
`APresoribe,,_3
`APrescribe,.,,,,
`APrescribe,,_,5
`APrescribe,,,,5
`
`Acompefiforgi
`Acompeiitor,-,1
`Acompeiitor,-,,2
`ACompeti!or,,,3
`ACompeti!or,»,,,,
`A00mpeIr'1‘0r,»,,5
`AC0mpefitor,,,_6
`F-statistic
`
`0.120
`0.103
`0.062
`0.065
`0.047
`0.003
`0.016
`0.018
`0.002
`0.006
`0.006
`0.004
`0.007
`-0.003
`0.357
`0.205
`0.111
`0.040
`0. 004
`0.017
`
`0.25
`-0.041
`-0.038
`-0.032
`-0.012
`-0.002
`-0.006
`F(5