Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning About Drug Quality

Published Online:https://doi.org/10.1287/mksc.1100.0624

References

  • Arkes H. R. Costs and benefits of judgment errors: Implications for debiasing. Psych. Bull. (1991) 110(3):486–498CrossrefGoogle Scholar
  • Baumeister R. F., Bratslavsky E., Finkenauer C., Vohs K. D. Bad is stronger than good. Rev. General Psych. (2001) 5(4):323–370CrossrefGoogle Scholar
  • Bornstein B. H., Emler A. C., Chapman G. B. Rationality in medical treatment decisions: Is there a sunk-cost effect? Soc. Sci. Med. (1999) 49(2):215–222CrossrefGoogle Scholar
  • Boulding W., Kalra A., Staelin R. The quality double whammy. Marketing Sci. (1999) 18(4):463–484LinkGoogle Scholar
  • Bradley C. P. Commentary: Can we avoid bias? British Medical J. (2005) 330(April):784CrossrefGoogle Scholar
  • Brewin T. Primum non nocere? Lancet (1994) 344(8935):1487–1488CrossrefGoogle Scholar
  • Cacioppo J. T., Gardner W. L. Emotion. Annual Rev. Psych. (1999) 50(February):191–214CrossrefGoogle Scholar
  • Calverley P. M., Anderson J. A., Celli B., Ferguson G. T., Jenkins C., Jones P. W., Yates J. C., Vestbo J. Salmeterol and fluticasone propionate and survival in chronic obstructive pulmonary disease. New Engl. J. Med. (2007) 356(8):775–789CrossrefGoogle Scholar
  • Camerer C. F., Loewenstein G.Behavioral Economics: Past, Present, Future (2004) (Princeton University Press, Princeton, NJ) CrossrefGoogle Scholar
  • Casella G., George E. I. Explaining the Gibbs sampler. Amer. Statistician (1992) 46(3):167–174Google Scholar
  • Chan T., Narasimhan C., Xie Y. An empirical model of physician learning on treatment effectiveness and side-effects. (2010) . Working paper, Washington University in St. Louis, St. LouisGoogle Scholar
  • Chib S., Greenberg E. Understanding the Metropolis-Hastings algorithm. Amer. Statistician (1995) 49(4):327–335Google Scholar
  • Ching A., Ishihara M. The effects of detailing on prescribing decisions under quality uncertainty. Quant. Marketing Econom. (2010) 8(2):123–165CrossrefGoogle Scholar
  • Chintagunta P. K., Jiang R., Jin G. Z. Information, learning, and drug diffusion: The case of Cox-2 inhibitors. Quant. Marketing Econom. (2009) 7(4):399–443CrossrefGoogle Scholar
  • Coscelli A. The importance of doctors' and patients' preferences in the prescription decision. J. Indust. Econom. (2000) 48(3):349–369CrossrefGoogle Scholar
  • Coscelli A., Shum M. An empirical model of learning and patient spillovers in new drug entry. J. Econometrics (2004) 122(2):213–246CrossrefGoogle Scholar
  • Crawford G. S., Shum M. Uncertainty and learning in pharmaceutical demand. Econometrica (2005) 73(4):1137–1173CrossrefGoogle Scholar
  • Croskerry P. Achieving quality in clinical decision making: Cognitive strategies and detection of bias. Acad. Emergency Med. (2002) 9(11):1184–1204CrossrefGoogle Scholar
  • Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad. Med. (2003) 78(8):775–780CrossrefGoogle Scholar
  • Datamonitor Forecast insight: Asthma/COPD—Little long-term future for long-acting single-agent inhalers? (2008) . Report DMHC2439, Datamonitor, LondonGoogle Scholar
  • DeGroot M. H.Optimal Statistical Decisions (1970) (John Wiley & Sons, Hoboken, NJ) Google Scholar
  • Elstein A. S., Schwartz A. Clinical problem solving and diagnostic decision making: Selective review of the cognitive literature. British Medical J. (2002) 324(7339):729–732CrossrefGoogle Scholar
  • Epstein L. G. An axiomatic model of non-Bayesian updating. Rev. Econom. Stud. (2006) 73(2):413-436CrossrefGoogle Scholar
  • Erdem T., Keane M. P. Decision-making under uncertainty: Capturing dynamic brand choice processes in turbulent consumer goods markets. Marketing Sci. (1996) 15(1):1–20LinkGoogle Scholar
  • Estrada C. A., Isen A. M., Young M. J. Positive affect facilitates integration of information and decreases anchoring in reasoning among physicians. Organ. Behav. Human Decision Processes (1997) 72(1):117–135CrossrefGoogle Scholar
  • Fiscella K., Franks P., Zwanziger J., Mooney C., Sorbero M., Williams G. C. Risk aversion and costs: A comparison of family physicians and general internists. J. Family Practice (2000) 49(1):12–17Google Scholar
  • Geweke J., Bernardo J. M., Berger J. O., Dawid A. P., Smith A. F. M. Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments. Bayesian Statistics (1992) 4(Oxford University Press, Oxford, UK) 169–193Google Scholar
  • Global Initiative for Asthma (GINA) Global strategy for asthma management and prevention. (2008) . Report, GINA, http://www.ginasthma.com/Guidelineitem.asp??/1=2&/2=1&intId=60Google Scholar
  • Goldenberg J., Libai B., Moldovan S., Muller E. The NPV of bad news. Internat. J. Res. Marketing (2007) 24(3):186–200CrossrefGoogle Scholar
  • Griffin D., Tversky A. The weighing of evidence and the determinants of confidence. Cognitive Psych. (1992) 24(3):411–435CrossrefGoogle Scholar
  • Hall K. H. Reviewing intuitive decision-making and uncertainty: The implications for medical education. Medical Ed. (2002) 36(3):216–224CrossrefGoogle Scholar
  • Häubl G., Dellaert B. G. C., Donkers B. Tunnel vision: Local behavioral influences on consumer decisions in product search. Marketing Sci. (2010) 29(3):438–455LinkGoogle Scholar
  • Ho T. H., Lim N., Camerer C. F. Modeling the psychology of consumer and firm behavior with behavioral economics. J. Marketing Res. (2006) 43(3):307–331CrossrefGoogle Scholar
  • Kass R. E., Raftery A. E. Bayes factors. J. Amer. Statist. Assoc. (1995) 90(430):773–795CrossrefGoogle Scholar
  • Klein J. G. Five pitfalls in decisions about diagnosis and prescribing. British Medical J. (2005) 330(7494):781–783CrossrefGoogle Scholar
  • Marks N. A., Ind P. W., Gibson P. G., Abramson M., Costabel U., Hensley M., Volmink J., Wood-Baker R. Novel therapies in asthma: Long-acting β2-agonists/inhaled corticosteroids. Evidence-Based Respiratory Medicine (2005) (Blackwell Publishing, Malden, MA) 217–230CrossrefGoogle Scholar
  • McCulloch R., Rossi P. E. An exact likelihood analysis of the multinomial probit model. J. Econometrics (1994) 64(1–2):207–240CrossrefGoogle Scholar
  • Medin D. L., Altom M. W., Edelson S. M., Freko D. Correlated symptoms and simulated medical classification. J. Experiment. Psych.: Learn., Memory, Cognition (1982) 8(1):37–50CrossrefGoogle Scholar
  • Mehta N., Chen X. J., Narasimhan O. Informing, transforming, and persuading: Disentangling the multiple effects of advertising on brand choice decisions. Marketing Sci. (2008) 27(3):334–355LinkGoogle Scholar
  • Mehta N., Rajiv S., Srinivasan K. Role of forgetting in memory-based choice decisions: A structural model. Quant. Marketing Econom. (2004) 2(2):107–140CrossrefGoogle Scholar
  • Mizerski R. W. An attribution explanation of the disproportionate influence of unfavorable information. J. Consumer Res. (1982) 9(3):301–310CrossrefGoogle Scholar
  • Narasimhan C., He C., Anderson E. T., Brenner L., Desai P., Kuksov D., Messinger P., et al. Incorporating behavioral anomalies in strategic models. Marketing Lett. (2005) 16(3/4):361–373CrossrefGoogle Scholar
  • Narayanan S., Manchanda P. Heterogeneous learning and the targeting of marketing communication for new products. Marketing Sci. (2009) 28(3):424–441LinkGoogle Scholar
  • Narayanan S., Manchanda P., Chintagunta P. K. Temporal differences in the role of marketing communication in new product categories. J. Marketing Res. (2005) 42(3):278–290CrossrefGoogle Scholar
  • Nisbett R. E., Krantzm D. H., Jepson C., Kunda Z. The use of statistical heuristics in everyday inductive reasoning. Psych. Rev. (1983) 90(4):339–363CrossrefGoogle Scholar
  • Oliver R. L. Cognitive, affective, and attribute bases of the satisfaction response. J. Consumer Res. (1993) 20(3):418–430CrossrefGoogle Scholar
  • Poses R. M., Anthony M. Availability, wishful thinking, and physicians' diagnostic judgments for patients with suspected bacteremia. Medical Decision Making (1991) 11(3):159–168CrossrefGoogle Scholar
  • Rabin M., Schrag J. L. First impressions matter: A model of confirmatory bias. Quart. J. Econom. (1999) 114(1):37–82CrossrefGoogle Scholar
  • Raftery A. E., Lewis S. M., Bernardo J. M., Berger J. O., Dawid A. P., Smith A. F. M. How many iterations in the Gibbs sampler? Bayesian Statistics (1992) 4(Oxford University Press, Oxford, UK) 763–773Google Scholar
  • Redelmeier D. A. The cognitive psychology of missed diagnoses. Ann. Internal Med. (2005) 142(2):115–120CrossrefGoogle Scholar
  • Roberts J. H., Urban G. L. Modeling multiattribute utility, risk, and belief dynamics for new consumer durable brand choice. Management Sci. (1988) 34(2):167–185LinkGoogle Scholar
  • Rossi P. E., Allenby G. M., McCulloch R.Bayesian Statistics and Marketing (2005) (John Wiley & Sons, West Sussex, UK) CrossrefGoogle Scholar
  • Rozin P., Royzman E. B. Negativity bias, negativity dominance, and contagion. Personality Soc. Psych. Rev. (2001) 5(4):296–320CrossrefGoogle Scholar
  • Stremersch S. Health and marketing: The emergence of a new field of research. Internat. J. Res. Marketing (2008) 25(4):229–233CrossrefGoogle Scholar
  • Stremersch S., Van Dyck W. Marketing of the life sciences: A new framework and research agenda for a nascent field. J. Marketing (2009) 73(July):4–30CrossrefGoogle Scholar
  • Swait J., Louviere J. The role of the scale parameter in the estimation and comparison of multinomial logit models. J. Marketing Res. (1993) 30(3):305–314CrossrefGoogle Scholar
  • Tanner M. A., Wong W. H. The calculation of posterior distributions by data augmentation. J. Amer. Statist. Assoc. (1987) 82(398):528–540CrossrefGoogle Scholar
  • Taylor S. E. Asymmetrical effects of positive and negative events: The mobilization-minimization hypothesis. Psych. Bull. (1991) 110(1):67–85CrossrefGoogle Scholar
  • Tversky A., Kahneman D. Judgment under uncertainty: Heuristics and biases. Science (1974) 185(4157):1124–1131CrossrefGoogle Scholar
  • Venkataraman S., Stremersch S. The debate on influencing doctor's decisions: Are drug characteristics the missing link? Management Sci. (2007) 53(11):1688–1701LinkGoogle Scholar
  • Vlug A. E., van der Lei J., Mosseveld B. M., van Wijk M. A., van der Linden P. D., Sturkenboom M. C., van Bemmel J. H. Postmarketing surveillance based on electronic patient records: The IPCI project. Methods Inform. Med. (1999) 38(4–5):339–344Google Scholar
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