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Value-Based Pricing Alternatives for Personalised Drugs: Implications of Asymmetric Information and Competition

A Letter to the Editor to this article was published on 03 February 2020

A Letter to the Editor to this article was published on 03 February 2020


The market for new drugs is changing: personalised drugs will increase the heterogeneity in patients’ responses and, possibly, costs. In this context, price regulation will play an increasingly important role. In this article, we argue that personalised medicine opens new scenarios in the relationship between value-based prices, regulation and industry listing strategies. Our focus is on the role of asymmetry of information and competition. We show that, if the firm has more information than the payer on the heterogeneity in patients’ responses and it adopts a profit-maximising listing strategy, the outcome may be independent of the choice of the type of value-based price. In this case, the information advantage that the manufacturer has prevents the payer from using marginal value-based prices to extract part of the surplus. However, in a dynamic setting where competition by a new entrant is possible, the choice of the type of value-based price may matter. We suggest that more research should be devoted to the dynamic analysis of price regulation for personalised medicines.

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  1. OECD. Health at a glance 2013: OECD indicators. Paris: OECD Publishing; 2013.

    Book  Google Scholar 

  2. Carone G, Schwierz C, Xavier A. Cost-containment policies in public pharmaceutical spending in the EU. MPRA Paper:1-67. 2012.

  3. Panos K, Taylor D, Manning J, Carr M. Implementing value-based pricing for pharmaceuticals in the UK. 2020Health; 2010.

  4. OECD. Health at a glance 2011. Paris: OECD indicators; 2011.

    Book  Google Scholar 

  5. Wettermark B, Godman B, Jacobsson B, Haaijer-Ruskamp F. Soft regulations in pharmaceutical policymaking: an overview of current approaches and their consequences. Appl Health Econ Health Policy. 2009;7:137–47.

    Article  PubMed  Google Scholar 

  6. Kleinke JD. The price of progress: prescription drugs in the health care market. Health Aff. 2001;20:43–60.

    Article  CAS  Google Scholar 

  7. DiMasi JA, Hansen RW, Grabowski HG. The price of innovation: new estimates of drug development costs. J Health Econ. 2003;22:151–85.

    Article  PubMed  Google Scholar 

  8. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ. 2016;47:20–33.

    Article  PubMed  Google Scholar 

  9. Scannell JW, Blanckley A, Boldon H, Warrington B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov. 2012.

    Article  PubMed  Google Scholar 

  10. Schnee JE. R&D strategy in the US pharmaceutical industry. Res Policy. 1979;8(4):364–382.

    Article  Google Scholar 

  11. Danzon PM, Chao L-W. Does regulation drive out competition in pharmaceutical markets? J Law Econ. 2000;43:311–57.

    Article  Google Scholar 

  12. Bardey D, Bommier A, Jullien B. Retail price regulation and innovation: reference pricing in the pharmaceutical industry. J Health Econ. 2010;29:303–16.

    Article  CAS  PubMed  Google Scholar 

  13. Yu N, Helms Z, Bach PB. R&D costs for pharmaceutical companies do not explain elevated US drug prices. Health affairs blog. 2017. Retreived 28 November 2019.

  14. Walton SM, Basu A, Mullahy J, Hong S, Schumock GT. Measuring the value of pharmaceuticals in the US health system. Pharmacoeconomics. 2017;35:1–4.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Schork N. Personalized medicine: time for one-person trials. Nature. 2015;520:609–11.

    Article  CAS  PubMed  Google Scholar 

  16. Aitken M, Blansett L, Mawrie R. Developments in cancer treatments, market dynamics, patient access and value. Global oncology trend report. IMS Institute; 2015. P. 1–46.

  17. Davis JC, Furstenthal L, Desai AA, Norris T, Sutaria S, Fleming E, et al. The microeconomics of personalized medicine: today’s challenge and tomorrow’s promise. Nat Rev Drug Discov. 2009;8:279–81.

    Article  CAS  PubMed  Google Scholar 

  18. Eichler HG, Abadie E, Breckenridge A, Flamion B, Gustafsson LL, Leufkens H, et al. Bridging the efficacy-effectiveness gap: a regulator’s perspective on addressing variability of drug response. Nat Rev Drug Discov. 2011;10:495–506.

    Article  CAS  PubMed  Google Scholar 

  19. Gravelle HSE. Ex post value reimbursement for pharmaceuticals. Med Decis Mak. 1998;18:S27–38.

    Article  CAS  Google Scholar 

  20. Office of Fair Trade. The pharmaceutical price regulation scheme: an OFT market study. OFT: London; 2007.

    Google Scholar 

  21. Sussex J, Towse A, Devlin N. Operationalizing value-based pricing of medicines. Pharmacoeconomics. 2013;31:1–10.

    Article  PubMed  Google Scholar 

  22. Claxton K, Sculpher M, Carroll S. Value-based pricing for pharmaceuticals: its role, specification and prospects in a newly devolved NHS. CHE Research Paper 60. University of York; 2011, P. 1–27.

  23. Kaltenboeck A, Bach PB. Value-based pricing for drugs theme and variations. JAMA. 2018;319:2165–6.

    Article  PubMed  Google Scholar 

  24. Claxton K. OFT, VBP: QED? Health Econ. 2007;16:545–58.

    Article  PubMed  Google Scholar 

  25. Levaggi R, Pertile P. Pricing policies when patients are heterogeneous: a welfare analysis. Working Papers 17/2016. University of Verona, Department of Economics; 2016.

  26. Chandra A, Garthwaite C. The economics of indication-based drug pricing. N Engl J Med. 2017;377:103–6.

    Article  PubMed  Google Scholar 

  27. Bach PB. Indication-specific pricing for cancer drugs. JAMA. 2014;312:1629–30.

    Article  CAS  PubMed  Google Scholar 

  28. Ghislandi S, Kuhn M. Asymmetric information in the regulation of the access to markets. Department of Economics Working Paper Series, 219. Vienna: WU Vienna University of Economics and Business. P. 1–33.

  29. Hawkins N, Scott DA. Reimbursement and value-based pricing: stratified cost-effectiveness analysis may not be the last word. Health Econ. 2011;20:688–98.

    Article  PubMed  Google Scholar 

  30. Neri M, Towse A, Garau M. Multi-indication pricing (MIP): practical solutions and steps to move forward. Briefings 002084. Office of Health Economics; 2018.

  31. Towse A, Cole A, Zamora B. The debate on indication-based pricing in the U.S. and five major European countries. OHE consulting report. 2018.

  32. WHO. Pricing of cancer medicines and its impacts: a comprehensive technical report for the World Health Assembly Resolution 70.12: operative paragraph 2.9 on pricing approaches and their impacts on availability and affordability of medicines for the prevention and treatment of cancer. World Health Organization; 2018.

  33. Sul J, Blumenthal GM, Jiang X, He K, Keegan P, Pazdur R. FDA approval summary: pembrolizumab for the treatment of patients with metastatic non-small cell lung cancer whose tumors express programmed death-ligand 1. Oncologist. 2016;21:643–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Brahmer J, Reckamp KL, Baas P, et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med. 2015;373:123–35.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Carbognin L, Pilotto S, Milella M, et al. Differential activity of nivolumab, pembrolizumab and MPDL3280A according to the tumor expression of programmed death-ligand-1 (PD-L1): sensitivity analysis of trials in melanoma, lung and genitourinary cancers. PLoS One. 2015;10(6):e0130142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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The authors would like to thank the reviewers and the Editor for their helpful comments. The usual disclaimer applies.

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Correspondence to Rosella Levaggi.

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No sources of funding were received for the preparation of this article.

Conflict of Interest

Rosella Levaggi and Paolo Pertile have no conflicts of interest that are directly relevant to the content of this article.

Author Contributions

Both authors contributed to the development of the model described in Sect. 2 and to its draft. Rosella Levaggi wrote the first draft of the introduction while Paolo Pertile wrote the first draft of the Discussion and Conclusions. Both authors commented on previous versions of the manuscript. Both authors read and approved the final manuscript.



1.1 Marginal Value-Based Prices

Let us assume that the firm is about to list a drug that has been approved so that any sunk cost for its discovery has already been borne. If the firm decides to list only for the patients with the highest effectiveness, the price is \(\lambda E_{\text{H}}\) and the corresponding profit \(\varPi_{n}^{m} = \left( {\lambda E_{\text{H}} - c} \right)n\). If the firm asks for listing for both types of patients, price and profit are respectively, λEL and \(\varPi_{1}^{m} = \left( {\lambda E_{\text{L}} - c} \right)\). The firm chooses the alternative that allows the maximization of the profit by comparing,

$$\varPi_{n}^{m} = \left( {\lambda E_{\text{H}} - c} \right)n,$$


$$\varPi_{1}^{m} = \left( {\lambda E_{\text{L}} - c} \right).$$

We can write these conditions in terms of EH for choosing the first alternative:

$$\left( {\lambda E_{\text{H}} - c} \right)n - \left( {\lambda E_{\text{L}} - c} \right) > 0 ,$$
$$\lambda \left( {nE_{\text{H}} - E_{\text{L}} } \right) + c\left( {1 - n} \right) > 0,$$
$$E_{\text{H}} > \frac{{E_{\text{L}} }}{n} - \frac{{c\left( {1 - n} \right)}}{n\lambda }.$$

Hence, the maximum profit is,

$$\varPi_{n}^{m} = \left( {\lambda E_{\text{H}} - c} \right)n\quad {\text{if}}\;\;E_{\text{H}} \ge \frac{{E_{\text{L}} }}{n} - \frac{{c\left( {1 - n} \right)}}{n\lambda },$$
$$\varPi_{1}^{m} = \lambda E_{\text{L}} - c\quad {\text{if}}\;\;E_{\text{H}} < \frac{{E_{\text{L}} }}{n} - \frac{{c\left( {1 - n} \right)}}{n\lambda }.$$

1.2 Average Value-Based Prices

If the firm decides to list only for the patients with the highest effectiveness, the price will be equal to \(\lambda E_{\text{H}}\) and the profit will be \(\varPi_{n}^{a} = \left( {\lambda E_{\text{H}} - c} \right)n\). If the firm asks for listing for both types of patients, the price is \(\lambda E_{\text{A}} = \lambda \left( {nE_{\text{H}} + \left( {1 - n} \right)E_{\text{L}} } \right)\) and the profit is \(\varPi_{1}^{a} = \left( {\lambda E_{\text{A}} - c} \right)\). The firm chooses the alternative that allows the maximization of profit by comparing,

$$\varPi_{1}^{a} = \left( {\lambda E_{\text{A}} - c} \right),$$


$$\varPi_{n}^{a} = \left( {\lambda E_{\text{H}} - c} \right)n,$$

so that,

$$\varPi_{1}^{a} = \lambda \left( {nE_{\text{H}} + \left( {1 - n} \right)E_{\text{L}} } \right) - c,$$

which can be written as,

$$\varPi_{1}^{a} = n\left( {\lambda E_{\text{H}} - c} \right) + \left( {1 - n} \right)\left( {\lambda E_{\text{L}} - c} \right),$$
$$\varPi_{1}^{a} = \varPi_{n}^{a} + \left( {1 - n} \right)\left( {\lambda E_{\text{L}} - c} \right).$$

This proves that under average value-based prices, listing of both sub-groups is always preferred by the manufacturer.

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Levaggi, R., Pertile, P. Value-Based Pricing Alternatives for Personalised Drugs: Implications of Asymmetric Information and Competition. Appl Health Econ Health Policy 18, 357–362 (2020).

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