IPR2015-01100, No. 2207 Exhibit - Senju Exhibit 2207 (P.T.A.B. Feb. 25, 2016)

Are Physicians "Easy Marks"? Quantifying the Effects of Detailing and Sampling on New
`Prescriptions
`Author(s): Natalie Mizik and Robert Jacobson
`Source: Management Science, Vol. 50, No. 12 (Dec., 2004), pp. 1704-1715
`Published by: INFORMS
`Stable URL: http://www.jstor.org/stable/30048061
`Accessed: 20/01/2015 13:37
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`This content downloaded from 208.85.77.1 on Tue, 20 Jan 2015 13:37:52 PM
`All use subject to JSTOR Terms and Conditions
`
`Page 1 of 13
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`SENJU EXHIBIT 2207
`LUPIN v. SENJU
`IPR2015-01100
`
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`This content downloaded from 208.85.77.1 on Tue, 20 Jan 2015 13:37:52 PM
`All use subject to JSTOR Terms and Conditions
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`Page 2 of 13
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`All use subject to JSTOR Terms and Conditions
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`Page 4 of 13
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`All use subject to JSTOR Terms and Conditions
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`This content downloaded from 208.85.77.1 on Tue, 20 Jan 2015 13:37:52 PM
`All use subject to JSTOR Terms and Conditions
`
`Page 8 of 13
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`

`

`Mizik and Jacobson: Quantifying the Effects of Detailing and Sampling on New Prescriptions
`Management Science 50(l2), pp. 1704-1715, @2034 INFORMS
`
`1711
`
`Figure 1
`
`Direct Eliecls ot Detailing on the Prescribing Behavior nl Physicians
`
`Drug A
`
`
`
` Changeinthenumberofnewprescriptions
`
`rug B
`
`O D
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`
`
`Changeinthenumberofnewprescriptions
`
`
`
`_oOM
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`
`
` Changeinthenumberatnewprescriptions
`
`—0.01
`
`t
`
`t—i
`
`l—2
`
`t—3
`
`{—4
`
`1—5
`
`{—6
`
`Nate. Coefficient estimates from Equation (2). Numbers represent the additional number of new prescriptions a physician will issue in a given month following
`a one PSR visit increase in promotional effort occurring in the current month. Error bars represent 95% confidence intervals.
`
`estimated effects for months 0 through 6 are 0.054,
`0.033, 0.026, 0.023, 0.014, 0.002, and —0.001. These sum
`to a cumulative direct effect of 0.151 (std. = 0.029).
`Once we consider the persistence in the prescrib-
`ing process, the total effect of one detailing visit for
`Drug B is estimated at 0.32, with a 95% confidence
`interval of [0.219; 0.428]. In other words, on average
`it takes an additional 3.11 PSR visits to generate an
`additional new prescription for Drug B.
`For Drug C we again observe similar results in that
`the estimated effect of a PSR visit is statistically sig-
`nificant, but small in magnitude. The estimated effects
`for months 0 through 6 are 0.021, 0.028, 0.024, 0.021,
`0.012, 0.011, and 0.010, respectively. All estimates are
`statistically different from zero. The estimated cumu-
`lative direct effect of 0.129 (std. = 0.024) is the small-
`est of the three drugs studied. Furthermore, because
`Drug C prescriptions exhibit the lowest persistence,
`the total effect of one detailing visit for Drug C is also
`
`the smallest at 0.153, with a 95% confidence interval of
`[0.105; 0.201]. On average, it would take an additional
`6.54 PSR visits to induce one additional new prescrip-
`tion of Drug C.
`
`Sampling. We also observe statistically significant
`but small effects for sampling. Sampling for Drug A
`has a positive and statistically significant centempo-
`raneous effect (0.018), but statistically insignificant
`effects for months 1 through 6 (0.002, 0.006, 0.006,
`0.004, 0.007, and —0.003, respectively). The estimated
`cumulative direct effect across the six months is 0.041
`
`(std. = 0.02). The total (direct and indirect) effect of
`one free sample of Drug A is 0.155 with a 95% con-
`fidence interval of [0.032; 0.310]. As such, on average
`an additional 6.44 samples are needed to induce one
`additiOnal new prescription of Drug A.
`We see a smaller response for sampling for Drug B
`than for Drug A. A change in sampling has statistically
`
`This oomdownloaded fiom 208135.771 on Tue, 20 Jan 2015 13:37:52 PM
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`Page 9 of 13
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`

`

`1712
`
`Mizik and Jacobson: Quantifying the Eflects of Detailing and Sampling on New Prescriptions
`Management Science 50(12), pp 1704-1715, @2004 INFORMS
`
`Figure 2
`
`Direct Ellen: oi Samling on the Prescribing Behavior oi Physician:
`
`0.03
`
`Drug A
`
`
`
`
`
`I
`
`
`o
`-o.02
`
`
`
`0.02
`0.01
`
`g
`3 ..
`c .9-
`S in
`0 5
`"' 0
`s a
`0 3
`g a
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`
`
`
`
`Changeinthenurrberofnewprescriptions
`
`
`
`Changeinthenumberofnewprescriptions
`
`
`
`
`
`i
`
`{-1
`
`(-2
`
`i-3
`
`{—4
`
`l-S
`
`{—6
`
`Nate. Coefficient estimates irom Equation (2). Numbers represent the additional number of new prescriptions a physician will issue in a given month following
`a one tree drug sample increase in promotional eiiort occurring in the current month. Error bars represent 95% confidence intervals.
`
`significant positive, albeit declining, effects for each
`month (0.006, 0.003, 0.002, 0.002, 0.002, 0.002, and
`0.001 for months 0 through 6, respectively). The esti-
`mated cumulative direct effect across the six months
`is 0.019 (std. = 0.004). The total effect of one free sam-
`ple of Drug B is 0.039 with a 95% confidence interval
`of [0.025; 0.054]. it would take 25.39 additional sam-
`ples to generate one additional new prescription for
`Drug B.
`The estimated response to sampling is smallest for
`Drug C. The estimated effects are 0.007, 0.003, 0.001,
`0.0005, —0.0003, 0.00001, and 0.0001, with only the
`contemporaneous and one-month lag effects being
`statistically significant. The estimated cumulative
`direct effect is 0.012 (std. = 0.005). Because the persis-
`tence level is very low for Drug C, the total effect of
`one free drug sample is only slightly higher at 0.014,
`with a 95% confidence interval of [0.0042; 0.0232].
`
`This means that it takes 73.04 additional samples of
`Drug C to generate one new prescription.
`
`Competitive Prescriptions. As expected, consistent
`with brand switching we observe negative effects
`for lagged competitive prescriptions. The inclusion of
`these competitive effects in the model is important,
`not only in helping explain new prescriptions, but
`also in allowing us to isolate the persistence in physi-
`cian behaviors. That is, because competitive prescrip-
`tions are correlated with own prescriptions, failure to
`model these competitive effects would result in biased
`estimates of the autocorrelation coefficients and, as
`a result, biased estimates of total detailing and sam-
`pling effects.
`As discussed earlier, the instrumental variable for
`
`the contemporaneous competitive prescriptions cap-
`tures two distinct phenomena with two distinct
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`Page 10 of 13
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`All use subject to JSTOR Terms and Conditions
`
`Page 12 of 13
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`

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`Mizik and Jacobson: Quantifying the Efi'ecls of Detailing and Sampling on New Prescriptions
`Management Science 50(12), pp. 17044715, ©2004 INFORMS
`
`1715
`
`care or costs would not. However, responsiveness
`to PSRs could result in inferior patient care or in
`higher costs when physicians prescribe, for exam-
`ple, branded drugs that are no more effective than
`a generic equivalent but are priced higher. Whatever
`the relative costs and benefits, the bottom line remains
`
`that the average effect of PSR activity on physician
`prescribing behavior is modest.
`
`Acknowledgments
`Data used in this study were made available to the authors
`by a US pharmaceutical manufacturer with the only con-
`dition of ensuring the confidentiality of the company and
`the specific drugs in the study. The authors received no
`financial suppOrt for this research from the data provider
`or any other member of the pharmaceutical industry. The
`authors thank the anonymous company for granting access
`to the data, and the Institute for the Study of Business Mar-
`kets (lSBM) at Pennsylvania State University for providing a
`research grant as part of the 2001 ISBM Business Marketing
`Doctoral Student Support Award Competition. The authors
`also thank Pierre Azoulay, Department Editor Linda Green,
`the associate editor, and the referees for their comments.
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