Physicians' Persistence and Its Implications for Their Response to Promotion of Prescription Drugs

Abstract

Motivated by the medical literature findings that physicians are inertial, we seek to understand (1) whether physicians exhibit structural persistence in drug choice (structural persistence occurs when the drug chosen for a patient depends structurally on the drug previously prescribed by the physician to other patients) and (2) whether persistence, if present, is a physician-specific characteristic or a physician state that can change over time. We further explore the role of promotional tools on persistence and drug choice, and we investigate whether physicians who exhibit persistence respond differently to three forms of sales promotion: one-to-one meetings (detailing), out-of-office meetings, and symposium meetings. Our results show significant levels of physician persistence in drug choice. We find that persistence is mostly a cross-sectional physician feature. Nonpersistent physicians appear to be responsive to detailing and symposium meetings, whereas persistent physicians seem to be responsive only to symposium meetings. Out-of-office meetings, such as golf or lunch, have no effect on physicians' drug choice. We also find that (1) older physicians and those who work in smaller practices are more likely to be persistent and (2) physicians who are more willing to receive sales force representatives have a lower likelihood of being persistent. Finally, we discuss implications for public policy from our rich set of results.decision analysis, physician prescription behavior, health care, econometrics

Authorized Generic Entry prior to Patent Expiry: Reassessing Incentives for Independent Generic Entry

“…The high frequency of early entry suggests that original drug producers primarily make use of early entry arrangements to mitigate the loss of monopoly profits. Original drug producers' advertising efforts in a given market are known to decline around the time of patent expiry and to drastically decline thereafter (Berndt et al, 2003;Janakiraman et al, 2008;EUC, 2009), suggesting that advertising is not an alternative channel through which original drug producers' financial distress would impact independent generic entry decisions.…”

“…Pharmaceutical firms spend a substantial fraction of profits on advertising 25 , yet these data are private and for researchers almost impossible to acquire. Pre-expiration brand advertising may not be a barrier to generic entry (Morton, 2000) -original drug producers' intensity of advertising decreases drastically as exclusivity expires (Berndt et al, 2003;Janakiraman et al, 2008;EUC, 2009)-, and generic advertising may be rare (Scherer, 2000;Berndt et al, 2003), the lack of advertising data is certainly a limitation of this study. 23 Strength, drug form and therapeutic field(s) of indication are specified.…”

Modeling coexisting business scenarios with time-series panel data: A dynamics-based segmentation approach

“…Mizik and Jacobson (2004) employed fixed-effects instrumental variable estimation to address both heterogeneity and endogeneity in physician response to direct-to-physician marketing activities. Finally, Manchanda (2005), Janakiraman, Dutta, Sismeiro, andStern (2008), and Janakiraman, Sismeiro, and Dutta (2009) used physician panel data to estimate an individual-level model of prescription choice within a therapeutic class. Although these studies did not investigate the evolving nature of the corresponding prescription and marketing mix series, these authors find significant differences across physicians in response to pharmaceutical detailing, in choice state-dependence, and in learning rates.…”

Can branded drugs benefit from generic entry? The role of detailing and price in switching to non-bioequivalent molecules

“…As a result, we can account for the full competitive actions when modeling drug choice conditional on a prescription using a multinomial choice model. Finally, previous studies of pharmaceuticals have shown that, at a given patient visit, physicians are influenced by their own previous prescription choices (e.g., Janakiraman, Dutta, Sismeiro, & Stern, 2008). As a result, by modeling the prescription decision for each individual patient, we can account for these carryover effects.…”

“…We tested for alternative decay values (details presented in the Robustness checks section). measurement of marketing and price responsiveness (Janakiraman, Dutta, et al, 2008;Janakiraman, Sismeiro, et al, in press). Following previous research, we incorporate information about physicians' past prescriptions using a dummy variable, SX ijt , that takes the value of one if physician i selects drug j on prescribing occasion t − 1 and takes the value of zero otherwise (we have tested alternative state-dependence specifications and found that the lagged dummy provides the best fit; details available from the authors upon request).…”