Health economic evaluation has become increasingly important in medical research and recently has been built on solid statistical and decision-theoretic foundations, particularly under the Bayesian approach. In this article we review the basic concepts and issues associated with the statistical and decision-theoretic components of health economic evaluations. We present examples of typical models used in different contexts (depending on the availability of data). We also describe the process of uncertainty analysis, a crucial component of economic evaluations for health care interventions, aimed at assessing the impact of uncertainty in the model parameters on the final decision-making process. Finally, we discuss some of the most recent methodological developments, related with the application of advanced statistical models (e.g., Gaussian process regression) to facilitate the application of computationally expensive tools such as value of information analysis.


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Literature Cited

  1. Ades A, Lu G, Claxton K. 2004. Expected value of sample information calculations in medical decision modeling. Med. Decis. Making 24:207–27 [Google Scholar]
  2. Ades A, Sutton A. 2006. Multiparameter evidence synthesis in epidemiology and medical decision making: current approaches. J. R. Stat. Soc. A 169:5–35 [Google Scholar]
  3. Baio G. 2012. Bayesian Methods in Health Economics Boca Raton, FL: Chapman and Hall/CRC [Google Scholar]
  4. Baio G. 2014. Bayesian models for cost-effectiveness analysis in the presence of structural zero costs. Stat. Med. 33:1900–13 [Google Scholar]
  5. Baio G, Dawid P. 2011. Probabilistic sensitivity analysis in health economics. Stat. Methods Med. Res. 24:6615–34 [Google Scholar]
  6. Baio G, Heath A. 2017. When simple becomes complicated: why Excel should lose its place at the top table. Glob. Reg. Health Technol. Assess. 4:1e3–e6 [Google Scholar]
  7. Baio G, Berardi A, Heath A. 2017. Bayesian Cost-Effectiveness Analysis with the R Package BCEA Cham, Switz.: Springer [Google Scholar]
  8. Bernardo J, Smith A. 1999. Bayesian Theory New York: Wiley [Google Scholar]
  9. Brennan A, Kharroubi S, O'Hagan A, Chilcott J. 2007. Calculating partial expected value of perfect information via Monte Carlo sampling algorithms. Med. Decis. Mak. 27:448–70 [Google Scholar]
  10. Briggs A, Sculpher M, Claxton K. 2006. Decision Modelling for Health Economic Evaluation Oxford, UK: Oxford Univ. Press [Google Scholar]
  11. Claxton K. 1999. The irrelevance of inference: a decision-making approach to stochastic evaluation of health care technologies. J. Health Econ. 18:342–64 [Google Scholar]
  12. Claxton K, Martin S, Soares M, Rice N, Spackman E. et al. 2015. Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold. Health Technol. Assess. 19:141–503 [Google Scholar]
  13. Claxton K, Neumann P, Araki S, Weinstein M. 2001. Bayesian value-of-information analysis. Int. J. Technol. Assess. Health Care 17:38–55 [Google Scholar]
  14. Coyle D, Oakley J. 2008. Estimating the expected value of partial perfect information: a review of methods. Eur. J. Health Econ. 9:3251–59 [Google Scholar]
  15. Cressie N. 1993. Statistics for Spatial Data New York: Wiley [Google Scholar]
  16. Dias S, Sutton A, Welton N, Ades A. 2011. NICE DSU Technical Support Document 3: Heterogeneity: Subgroups, Meta-Regression, Bias and Bias-adjustment London: Natl. Inst. Health Care Excell. [Google Scholar]
  17. Elbasha E. 2005. Risk aversion and uncertainty in cost-effectiveness analysis: the expected-utility, moment-generating function approach. Health Econ 14:5457–70 [Google Scholar]
  18. Felli J, Hazen G. 1998. Sensitivity analysis and the expected value of perfect information. Med. Decis. Mak. 18:95–109 [Google Scholar]
  19. Felli J, Hazen G. 1999. A Bayesian approach to sensitivity analysis. Health Econ 8:263–68 [Google Scholar]
  20. Hastie T, Tibshirani R. 1990. Generalized Additive Models Boca Raton, FL: Chapman and Hall/CRC [Google Scholar]
  21. Heath A, Manolopoulou I, Baio G. 2016. Estimating the expected value of partial perfect information in health economic evaluations using integrated nested Laplace approximation. Stat. Med. 35:234264–80 [Google Scholar]
  22. Heath A, Manolopoulou I, Baio G. 2017. A review of methods for the analysis of the expected value of information. Med. Decis. Mak. 37:7747–58 [Google Scholar]
  23. Howard RA. 1966. Information value theory. IEEE Trans. Syst. Sci. Cybern. 2:22–26 [Google Scholar]
  24. Koerkamp B, Hunink M, Stijnen T, Hammitt J, Kuntz K, Weinstein M. 2007. Limitations of acceptability curves for presenting uncertainty in cost-effectiveness analyses. Med. Decis. Mak. 27:2101–11 [Google Scholar]
  25. Lindgren F, Rue H, Lindström J. 2011. An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach. J. R. Stat. Soc. B 73:4423–98 [Google Scholar]
  26. Lindley D. 1985. Making Decisions London: Wiley, 2nd ed.. [Google Scholar]
  27. Lindley D. 2006. Understanding Uncertainty New York: Wiley [Google Scholar]
  28. Loomes G, McKenzie L. 1989. The use of QALYs in health care decision making. Soc. Sci. Med. 28:299–308 [Google Scholar]
  29. Madan J, Ades A, Price M, Maitland K, Jemutai J. et al. 2014. Strategies for efficient computation of the expected value of partial perfect information. Med. Decis. Mak. 34:3327–42 [Google Scholar]
  30. Minelli C, Baio G. 2015. Value of information: a tool to improve research prioritization and reduce waste. PLOS Med 12:9e1001882 [Google Scholar]
  31. Nixon R, Thompson S. 2005. Incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations. Health Econ 14:1217–29 [Google Scholar]
  32. O'Hagan A, Stevens J. 2001. A framework for cost-effectiveness analysis from clinical trial data. Health Econ 10:303–15 [Google Scholar]
  33. O'Hagan A, Stevens J, Montmartin J. 2001. Bayesian cost effectiveness analysis from clinical trial data. Stat. Med. 20:733–53 [Google Scholar]
  34. Phillippo D, Ades A, Dias S, Palmer S, Abrams K, Welton N. 2016. NICE DSU Technical Support Document 18: Methods for Population-Adjusted Indirect Comparisons in Submissions to NICE London: Natl. Inst. Health Care Excell. [Google Scholar]
  35. Rasmussen C, Williams C. 2006. Gaussian Processes for Machine Learning Cambridge, MA: MIT Press [Google Scholar]
  36. Rue H, Held L. 2005. Gaussian Markov Random Field: Theory and Applications Boca Raton, FL: Chapman and Hall/CRC [Google Scholar]
  37. Rue H, Martino S, Chopin N. 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. J. R. Stat. Soc. B 71:2319–92 [Google Scholar]
  38. Rue H, Riebler A, Sørbye S, Illian J, Simpson D, Lindgren F. 2016. Bayesian computing with INLA: a review. Annu. Rev. Stat. Appl. 4:395–421 [Google Scholar]
  39. Sadatsafavi M, Bansback N, Zafari Z, Najafzadeh M, Marra C. 2013. Need for speed: an efficient algorithm for calculation of single-parameter expected value of partial perfect information. Value Health 16:2438–48 [Google Scholar]
  40. Spiegelhalter D, Abrams K, Myles J. 2004. Bayesian Approaches to Clinical Trials and Health-Care Evaluation Chichester, UK: Wiley [Google Scholar]
  41. Stinnett A, Mullahy J. 1998. Net health benefits: a new framework for the analysis of uncertainty in cost effectiveness analysis. Med. Decis. Mak. 18:S68–80 [Google Scholar]
  42. Strong M, Oakley J. 2013. An efficient method for computing single-parameter partial expected value of perfect information. Med. Decis. Mak. 33:6755–66 [Google Scholar]
  43. Strong M, Oakley J, Brennan A. 2014. Estimating multiparameter partial expected value of perfect information from a probabilistic sensitivity analysis sample: a nonparametric regression approach. Med. Decis. Mak. 34:3311–26 [Google Scholar]
  44. Strong M, Oakley J, Brennan A, Breeze P. 2015. Estimating the expected value of sample information using the probabilistic sensitivity analysis sample: a fast nonparametric regression-based method. Med. Decis. Mak. 35:5570–83 [Google Scholar]
  45. Van Hout B Al M, Gordon G, Ruten F. 1994. Costs, effects and cost-effectiveness-ratios alongside a clinical-trial. Health Econ 3:309–19 [Google Scholar]
  46. Welton N, Sutton A, Cooper N, Abrams K. 2012. Evidence Synthesis for Decision Making in Healthcare Chichester, UK: Wiley [Google Scholar]
  47. Welton N, Thom H. 2015. Value of information: We've got speed, what more do we need?. Med. Decis. Mak. 35:5564–66 [Google Scholar]
  48. Willan A, Briggs A. 2006. The Statistical Analysis of Cost-Effectiveness Data Chichester, UK: Wiley [Google Scholar]
  49. Willan A, Briggs A, Hock J. 2004. Regression methods for covariate adjustment and subgroup analysis for non-censored cost-effectiveness data. Health Econ 13:461–75 [Google Scholar]
  50. Williams C, Lewsey J, Briggs A, Mackay D. 2016. Estimation of survival probabilities for use in cost-effectiveness analysis: a comparison of a multi-state modelling survival analysis approach with partitioned survival and Markov decision-analytic modelling. Med. Decis. Mak. 37:427–39 [Google Scholar]
  51. Wilson E. 2015. A practical guide to value of information analysis. PharmacoEconomics 33:2105–21 [Google Scholar]
  52. Zivin JG. 2001. Cost-effectiveness analysis with risk aversion. Health Econ 10:6499–508 [Google Scholar]

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