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Value of Information and Pricing New Healthcare Interventions

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Abstract

Previous application of value-of-information methods to optimal clinical trial design have predominantly taken a societal decision-making perspective, implicitly assuming that healthcare costs are covered through public expenditure and trial research is funded by government or donation-based philanthropic agencies. In this paper, we consider the interaction between interrelated perspectives of a societal decision maker (e.g. the National Institute for Health and Clinical Excellence [NICE] in the UK) charged with the responsibility for approving new health interventions for reimbursement and the company that holds the patent for a new intervention. We establish optimal decision making from societal and company perspectives, allowing for trade-offs between the value and cost of research and the price of the new intervention.

Given the current level of evidence, there exists a maximum (threshold) price acceptable to the decision maker. Submission for approval with prices above this threshold will be refused. Given the current level of evidence and the decision maker’s threshold price, there exists a minimum (threshold) price acceptable to the company. If the decision maker’s threshold price exceeds the company’s, then current evidence is sufficient since any price between the thresholds is acceptable to both. On the other hand, if the decision maker’s threshold price is lower than the company’s, then no price is acceptable to both and the company’s optimal strategy is to commission additional research. The methods are illustrated using a recent example from the literature.

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References

  1. Ades AE, Lu G, Claxton K. Expected value of sample information calculations in medical decision modeling. Med Decis Making 2004; 24: 207–27

    Article  PubMed  CAS  Google Scholar 

  2. Claxton K, Posnett J. An economic approach to clinical trial design and research priority setting. Health Econ 1996; 5: 513–24

    Article  PubMed  CAS  Google Scholar 

  3. Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ 1999; 18: 341–64

    Article  PubMed  CAS  Google Scholar 

  4. Claxton K, Lacey LF, Walker SG. Selecting treatments; a decision theoretic approach. J Roy Stat Soc A Sta 2000; 163: 211–26

    Article  Google Scholar 

  5. Claxton K, Thompson KM. A dynamic programming approach to the efficient design of clinical trials. J Health Econ 2001; 20: 797–822

    Article  PubMed  CAS  Google Scholar 

  6. Eckermann S, Willan AR. Expected value of information and decision making in HTA. Health Econ 2007; 16: 195–209

    Article  PubMed  Google Scholar 

  7. Eckermann S, Willan AR. Time and EVSI wait for no patient. Value Health 2008; 11: 522–6

    Article  PubMed  Google Scholar 

  8. Eckermann S, Willan AR. The option value of delay in health technology assessment. Med Decis Making 2008; 28: 300–5

    Article  PubMed  Google Scholar 

  9. Eckermann S, Willan AR. Globally optimal trial design for local decision making. Health Econ 2009; 18: 203–16

    Article  PubMed  Google Scholar 

  10. Eckermann S, Karnon J, Willan AR. The value of value of information: best informing research design and prioritization using current methods. Pharmacoeconomics 2010; 28 (9): 699–709

    Article  PubMed  Google Scholar 

  11. Gittins J, Pezeshk H. How large should a trial be? Statistician 2000; 49: 177–97

    Google Scholar 

  12. Gittins J, Pezeshk H. A behavioral Bayes method for determining the size of a clinical trial. Drug Inf J 2000; 34: 355–63

    Google Scholar 

  13. Halpern J, Brown Jr BW, Hornberger J. The sample size for a clinical trial: a Bayesian-decision theoretic approach. Stat Med 2001; 20: 841–58

    Article  PubMed  CAS  Google Scholar 

  14. Hornberger JC, Brown Jr BW, Halpern J. Designing a cost-effective clinical trial. Stat Med 1995; 14: 2249–59

    Article  PubMed  CAS  Google Scholar 

  15. Hornberger J, Eghtesady P. The cost-benefit of a randomized trial to a health care organization. Control Clin Trials 1998; 19: 198–211

    Article  PubMed  CAS  Google Scholar 

  16. Kikuchi T, Pezeshk H, Gittins J. A Bayesian cost-benefit approach to the determination of sample size in clinical trials. Stat Med 2008; 27: 68–82

    Article  PubMed  Google Scholar 

  17. Pezeshk H, Gittins J. A fully Bayesian approach to calculating sample sizes for clinical trials with binary response. Drug Inf J 2002; 36: 143–50

    Article  Google Scholar 

  18. Pezeshk H. Bayesian techniques for sample size determination in clinical trials: a short review. Stat Methods Med Res 2003; 12: 489–504

    Article  PubMed  Google Scholar 

  19. Welton NJ, Ades AE, Caldwell DM, et al. Research prioritization based on expected value of partial perfect information: a case-study on interventions to increase uptake of breast cancer screening (with discussion). J Roy Stat Soc C-App 2008; 171: 807–41

    Article  Google Scholar 

  20. Willan AR, Pinto EM. The expected value of information and optimal clinical trial design [erratum appears in Stat Med 2006; 25: 720]. Stat Med 2005; 24: 1791–806

    Article  PubMed  Google Scholar 

  21. Willan AR. Clinical decision making and the expected value of information. Clin Trials 2007; 4: 279–85

    Article  PubMed  Google Scholar 

  22. Willan AR. Optimal sample size determinations from an industry perspective based on the expected value of information. Clin Trials 2008; 5: 587–94

    Article  PubMed  Google Scholar 

  23. Willan AR, Kowgier ME. Determining optimal sample sizes for multi-stage randomized clinical trials using value of information methods. Clin Trials 2008; 5: 289–300

    Article  PubMed  Google Scholar 

  24. Willan AR, Eckermann S. Optimal clinical trial design using value of information methods with imperfect implementation. Health Econ 2010; 19: 549–61

    PubMed  Google Scholar 

  25. Kikuchi T, Gittins J. A behavioral Bayes method to determine the sample size of a clinical trial considering efficacy and safety. Stat Med 2009; 28: 2293–306

    Article  PubMed  Google Scholar 

  26. Kikuchi T, Gittins J. A Bayesian adaptive design for the evaluation of a new drug in a bridging study. Biostat Bioinform and Biomath 2010; 1: 73–100

    Google Scholar 

  27. Kikuchi T, Gittins J. A behavioral Bayes approach to the determination of sample size for clinical trials considering efficacy and safety: imbalanced sample size in treatment groups. Stat Methods Med Res 2010; 20 (4): 389–400

    Article  PubMed  Google Scholar 

  28. Kikuchi T, Gittins J. A behavioral Bayes approach for sample size determination in cluster randomised clinical trials. JRSS, Series C 2011; 60: 1–14

    Google Scholar 

  29. Griffin SC, Claxton KP, Palmer SJ, et al. Dangerous omissions: the consequences of ignoring decision uncertainty. Health Econ 2011; 20 (2): 212–24

    Article  PubMed  Google Scholar 

  30. Chiba N, van Zanten SJ, Sinclair P, et al. Treating Helicobacter pylori infection in primary care patients with uninvestigated dyspepsia: the Canadian adult dyspepsia empiric treatment-Helicobacter pylori positive (CADET-Hp) randomised controlled trial. BMJ 2002; 324: 1012–6

    Article  PubMed  CAS  Google Scholar 

  31. Chiba N, Veldhuyzen Van Zanten SJ, Escobedo S, et al. Economic evaluation of Helicobacter pylori eradication in the CADET-Hp randomized controlled trial of H. pyloripositive primary care patients with uninvestigated dyspepsia. Aliment Pharmacol Ther 2004; 19 (3): 349–58

    Article  PubMed  CAS  Google Scholar 

  32. Willan AR. Incremental net benefit in the analysis of economic data from clinical trials with application to the CADET-Hp Trial. Eur J Gastroen Hepa 2004; 16: 543–9

    Article  Google Scholar 

  33. Griffin S, Claxton K, Sculpher M. Decision analysis for resource allocation in health care. J Health Ser Res Policy 2008; 13 Suppl. 3: 23–30

    Article  Google Scholar 

  34. Culyer AJ, McCabe C, Briggs A, et al. Searching for a threshold not setting one: the role of the National Institute for Health and Clinical Excellence. J Health Ser Res Policy 2007; 12: 56–8

    Article  Google Scholar 

  35. McCabe C, Claxton K, Culyer AJ. The NICE cost effectiveness threshold: what it is and what that means. Pharmacoeconomics 2008; 26 (9): 733–44

    Article  PubMed  Google Scholar 

  36. Claxton K, Buxton M, Culyer A, et al. Value based pricing for NHS drugs: an opportunity not to be missed? BMJ 2008; 336: 251–4

    Article  PubMed  Google Scholar 

  37. Pekarsky B. Should financial incentives be used to differentially reward ‘me-too’ and innovative drugs? Pharmacoeconomics 2010; 28 (1): 1–17

    Article  PubMed  Google Scholar 

  38. Eckermann S. Funding to maximise quality of care within a budget [working paper no. 5]. Adelaide (SA): Flinders Centre for Clinical Change and Health Care Research, 2009

    Google Scholar 

  39. Brennan A, Kharroubi SA. Expected value of sample information for Weibull survival data. Health Econ 2007; 16: 1205–25

    Article  PubMed  Google Scholar 

  40. Briggs AH, Mooney CZ, Wonderling DE. Constructing confidence intervals for cost-effectiveness ratios: an evaluation of parametric and non-parametric techniques using Monte Carlo simulation. Stat Med 1999; 18: 3245–62

    Article  PubMed  CAS  Google Scholar 

  41. Briggs A, Nixon R, Dixon S, et al. Parametric modelling of cost data: some simulation evidence. Health Econ 2005; 14: 421–8

    Article  PubMed  Google Scholar 

  42. Nixon RM, Wonderling D, Grieve RD. Non-parametric methods for cost-effectiveness analysis: the central limit theorem and the bootstrap compared. Health Econ 2010; 19: 316–33

    Article  PubMed  Google Scholar 

  43. Willan AR, Briggs AH, Hoch JS. Regression methods for covariate adjustment and subgroup analysis for non-censored cost-effectiveness data. Health Econ 2004; 13: 461–75

    Article  PubMed  Google Scholar 

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Acknowledgements

A.R. Willan is funded by the Discovery Grant Program of the Natural Sciences and Engineering Research Council of Canada (grant number 44868-08).

Both authors contributed to all aspects of the paper, except A.R. Willan is solely responsible for the algebraic solutions. A.R. Willan is the guarantor for the overall content of this paper.

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Authors and Affiliations

  1. SickKids Research Institute and University of Toronto, Toronto, Ontario, Canada

    Andrew R. Willan

  2. University of Wollongong, Wollongong, New South Wales, Australia

    Simon Eckermann

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  1. Andrew R. Willan
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  2. Simon Eckermann
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Correspondence to Andrew R. Willan.

Appendix

Appendix

\(\tilde{R}_m^d\) is the decision maker’s threshold price following a trial of m patients per arm. That is, \(\tilde{R}_m^d\) is that value of R, such that ∣max n {ENG m (n,R)}∣=0, where ENG(n,R) is the ENG of performing a trial of n patients per arm, once the evidence is updated with data from the trial of m patients per arm. Numerical integration with respect the distribution f is used to determine the expected value of \(\tilde{R}_m^d\), where f is the probability distribution function for the observed INB from the trial m patients per arm, which, under the assumptions we have made, is Normal with mean b 0 and variance v=b 0+σ 2+ /m.

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Willan, A.R., Eckermann, S. Value of Information and Pricing New Healthcare Interventions. PharmacoEconomics 30, 447–459 (2012). https://doi.org/10.2165/11592250-000000000-00000

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