1932

Abstract

Value of information (VoI) is a decision-theoretic approach to estimating the expected benefits from collecting further information of different kinds, in scientific problems based on combining one or more sources of data. VoI methods can assess the sensitivity of models to different sources of uncertainty and help to set priorities for further data collection. They have been widely applied in healthcare policy making, but the ideas are general to a range of evidence synthesis and decision problems. This article gives a broad overview of VoI methods, explaining the principles behind them, the range of problems that can be tackled with them, and how they can be implemented, and discusses the ongoing challenges in the area.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-statistics-040120-010730
2022-03-07
2024-12-09
Loading full text...

Full text loading...

/deliver/fulltext/statistics/9/1/annurev-statistics-040120-010730.html?itemId=/content/journals/10.1146/annurev-statistics-040120-010730&mimeType=html&fmt=ahah

Literature Cited

  1. Ades A, Lu G, Claxton K 2004. Expected value of sample information calculations in medical decision modeling. Med. Decis. Mak. 24:2207–27
    [Google Scholar]
  2. Andrianakis I, Vernon IR, McCreesh N, McKinley TJ, Oakley JE et al. 2015. Bayesian history matching of complex infectious disease models using emulation: a tutorial and a case study on HIV in Uganda. PLOS Comput. Biol. 11:1e1003968
    [Google Scholar]
  3. Arrow KJ, Lind RC. 1970. Uncertainty and the evaluation of public investment decisions. Am. Econ. Rev. 60:3364–78
    [Google Scholar]
  4. Baio G. 2018. Statistical modeling for health economic evaluations. Annu. Rev. Stat. Appl. 5:289–309
    [Google Scholar]
  5. Baio G, Berardi A, Heath A. 2017. Bayesian Cost-Effectiveness Analysis with the R Package BCEA New York: Springer
    [Google Scholar]
  6. Berger JO. 2013. Statistical Decision Theory and Bayesian Analysis New York: Springer
    [Google Scholar]
  7. Bernardo JM, Smith AFM. 1994. Bayesian Theory Chichester, UK: Wiley
    [Google Scholar]
  8. Borgonovo E, Hazen GB, Plischke E. 2016. A common rationale for global sensitivity measures and their estimation. Risk Anal 36:101871–95
    [Google Scholar]
  9. Borgonovo E, Plischke E. 2016. Sensitivity analysis: a review of recent advances. Eur. J. Oper. Res. 248:3869–87
    [Google Scholar]
  10. Brennan A, Chick S, Davies R. 2006. A taxonomy of model structures for economic evaluation of health technologies. Health Econ 15:121295–310
    [Google Scholar]
  11. Brennan A, Kharroubi SA. 2007a. Efficient computation of partial expected value of sample information using Bayesian approximation. J. Health Econ. 26:1122–48
    [Google Scholar]
  12. Brennan A, Kharroubi SA. 2007b. Expected value of sample information for Weibull survival data. Health Econ 16:111205–25
    [Google Scholar]
  13. Briggs A, Sculpher M, Claxton K. 2006. Decision modelling for health economic evaluation. In Handbooks in Health Economic Evaluation Oxford, UK: Oxford Univ. Press
    [Google Scholar]
  14. Briggs AH, Weinstein MC, Fenwick EAL, Karnon J, Sculpher MJ et al. 2012. Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–6. Value Health 15:6835–42
    [Google Scholar]
  15. Caflisch RE. 1998. Monte Carlo and quasi-Monte Carlo methods. Acta Numer 1998:1–49
    [Google Scholar]
  16. Caro JJ, Briggs AH, Siebert U, Kuntz KM. 2012. Modeling good research practices–overview: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force–1. Med. Decis. Mak. 32:5667–77
    [Google Scholar]
  17. Chaloner K, Verdinelli I. 1995. Bayesian experimental design: a review. Stat. Sci. 10:3273–304
    [Google Scholar]
  18. Chuang-Stein C, Kirby S, Hirsch I, Atkinson G 2011. The role of the minimum clinically important difference and its impact on designing a trial. Pharm. Stat. 10:3250–56
    [Google Scholar]
  19. Claxton K. 1999. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J. Health Econ. 18:3341–64
    [Google Scholar]
  20. Claxton K, Neumann PJ, Araki A, Weinstein MC 2001. Bayesian value-of-information analysis: an application to a policy model of Alzheimer's disease. Int. J. Technol. Assess. Health Care 17:138–55
    [Google Scholar]
  21. Claxton KP, Sculpher MJ. 2006. Using value of information analysis to prioritise health research. Pharmacoeconomics 24:111055–68
    [Google Scholar]
  22. Conti S, Claxton K. 2009. Dimensions of design space: a decision-theoretic approach to optimal research design. Med. Decis. Mak. 29:6643–60
    [Google Scholar]
  23. 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]
  24. Davis DR, Kisiel CC, Duckstein L. 1972. Bayesian decision theory applied to design in hydrology. Water Resourc. Res. 8:133–41
    [Google Scholar]
  25. De Angelis D, Presanis AM, Conti S, Ades AE. 2014. Estimation of HIV burden through Bayesian evidence synthesis. Stat. Sci. 29:19–17
    [Google Scholar]
  26. de Sá TH, Tainio M, Goodman A, Edwards P, Haines A et al. 2017. Health impact modelling of different travel patterns on physical activity, air pollution and road injuries for São Paulo, Brazil. Environ. Int. 108:22–31
    [Google Scholar]
  27. Drovandi CC, Holmes C, McGree JM, Mengersen K, Richardson S, Ryan EG 2017. Principles of experimental design for big data analysis. Stat. Sci. 32:3385
    [Google Scholar]
  28. Eidsvik J, Mukerji T, Bhattacharjya D. 2015. Value of Information in the Earth Sciences: Integrating Spatial Modeling and Decision Analysis Cambridge, UK: Cambridge Univ. Press
    [Google Scholar]
  29. Fang W, Wang Z, Giles MB, Welton NJ, Andrieu C et al. 2021. Multilevel and quasi Monte Carlo methods for the calculation of the expected value of partial perfect information Med. Decis. Mak. In press https://doi.org/10.1177/0272989X211026305
    [Crossref] [Google Scholar]
  30. Felli JC, Hazen GB. 1998. Sensitivity analysis and the expected value of perfect information. Med. Decis. Mak. 18:195–109
    [Google Scholar]
  31. Friedman JH. 1991. Multivariate adaptive regression splines. Ann. Stat. 19:1–67
    [Google Scholar]
  32. Giles MB, Goda T. 2019. Decision-making under uncertainty: using MLMC for efficient estimation of EVPPI. Stat. Comput. 29:4739–51
    [Google Scholar]
  33. Heath A, Kunst N, Jackson C, Strong M, Alarid-Escudero F et al. 2020. Calculating the expected value of sample information in practice: considerations from 3 case studies. Med. Decis. Mak. 40:3314–26
    [Google Scholar]
  34. 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]
  35. Heath A, Manolopoulou I, Baio G. 2017. A review of methods for analysis of the expected value of information. Med. Decis. Mak. 37:7747–58
    [Google Scholar]
  36. Heath A, Manolopoulou I, Baio G. 2019. Estimating the expected value of sample information across different sample sizes using moment matching and nonlinear regression. Med. Decis. Mak. 39:4347–59
    [Google Scholar]
  37. Hironaka T, Giles MB, Goda T, Thom H 2020. Multilevel Monte Carlo estimation of the expected value of sample information. SIAM/ASA J. Uncertain. Quantif. 8:31236–59
    [Google Scholar]
  38. Hughes D, Waddingham E, Mt-Isa S, Goginsky A, Chan E et al. 2016. Recommendations for benefit-risk assessment methodologies and visual representations. Pharmacoepidemiol. Drug Saf. 25:3251–62
    [Google Scholar]
  39. Ishizaka A, Nemery P. 2013. Multi-Criteria Decision Analysis: Methods and Software New York: Wiley
    [Google Scholar]
  40. Jackson CH, Bojke L, Thompson SG, Claxton K, Sharples LD. 2011. A framework for addressing structural uncertainty in decision models. Med. Decis. Mak. 31:4662–74
    [Google Scholar]
  41. Jackson CH, Presanis AM, Conti S, De Angelis D. 2019. Value of information: sensitivity analysis and research prioritisation in Bayesian evidence synthesis. J. Am. Stat. Assoc. 114:1436–49
    [Google Scholar]
  42. Jalal H, Alarid-Escudero F. 2018. A Gaussian approximation approach for value of information analysis. Med. Decis. Mak. 38:2174–88
    [Google Scholar]
  43. Johnson R, Woodcock J, de Nazelle A, de Sa T, Goel R et al. 2019. A guide to value of information methods for prioritising research in health impact modelling. arXiv:1905.00008 [stat.ME]
  44. Jones M, Goldstein M, Jonathan P, Randell F 2016. Bayes linear analysis for Bayesian optimal experimental design. J. Stat. Plann. Inference 171:115–29
    [Google Scholar]
  45. Koffijberg H, Rothery C, Chalkidou K, Grutters J 2018. Value of information choices that influence estimates: a systematic review of prevailing considerations. Med. Decis. Mak. 38:7888–900
    [Google Scholar]
  46. Kunst N, Wilson E, Glynn D, Alarid-Escudero F, Baio G et al. 2020. Computing the expected value of sample information efficiently: practical guidance and recommendations for four model-based methods. Value Health 23:6734–42
    [Google Scholar]
  47. Laber EB, Meyer NJ, Reich BJ, Pacifici K, Collazo JA, Drake JM. 2018. Optimal treatment allocations in space and time for on-line control of an emerging infectious disease. J. R. Stat. Soc. Ser. C 67:4743
    [Google Scholar]
  48. Lindley DV. 1956. On a measure of the information provided by an experiment. Ann. Math. Stat. 27:4986–1005
    [Google Scholar]
  49. Madan J, Ades AE, 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]
  50. Manski CF. 2019. Treatment choice with trial data: statistical decision theory should supplant hypothesis testing. Am. Stat. 73:Suppl. 1296304
    [Google Scholar]
  51. Menzies NA. 2016. An efficient estimator for the expected value of sample information. Med. Decis. Mak. 36:3308–20
    [Google Scholar]
  52. Milborrow S. 2021. earth: multivariate adaptive regression splines. R Package https://CRAN.R-project.org/package=earth
    [Google Scholar]
  53. Morio J. 2011. Global and local sensitivity analysis methods for a physical system. Eur. J. Phys. 32:61577
    [Google Scholar]
  54. Mueller N, Rojas-Rueda D, Cole-Hunter T, De Nazelle A, Dons E et al. 2015. Health impact assessment of active transportation: a systematic review. Prev. Med. 76:103–14
    [Google Scholar]
  55. Mytton OT, Jackson C, Steinacher A, Goodman A, Langenberg C et al. 2018. The current and potential health benefits of the National Health Service Health Check cardiovascular disease prevention programme in England: a microsimulation study. PLOS Med 15:3e1002517
    [Google Scholar]
  56. Natl. Inst. Health Care Excell 2013. Guide to the Methods of Technology Appraisal 2013 London: Natl. Inst. Health Care Excell.
    [Google Scholar]
  57. Nutt DJ, King LA, Phillips LD. 2010. Drug harms in the UK: a multicriteria decision analysis. Lancet 376:97521558–65
    [Google Scholar]
  58. Oakley JE, Brennan A, Tappenden P, Chilcott J. 2010. Simulation sample sizes for Monte Carlo partial EVPI calculations. J. Health Econ. 29:3468–77
    [Google Scholar]
  59. Oakley JE, O'Hagan A. 2004. Probabilistic sensitivity analysis of complex models: a Bayesian approach. J. R. Stat. Soc. Ser. B 66:3751–69
    [Google Scholar]
  60. O'Hagan A, Buck CE, Daneshkhah A, Eiser JR, Garthwaite PH et al. 2006. Uncertain Judgements: Eliciting Experts' Probabilities New York: Wiley
    [Google Scholar]
  61. O'Hagan A, Oakley JE. 2004. Probability is perfect, but we can't elicit it perfectly. Reliability Eng. Syst. Saf. 85:1–3239–48
    [Google Scholar]
  62. Parmigiani G, Inoue L. 2009. Decision Theory: Principles and Approaches Chichester, UK: Wiley
    [Google Scholar]
  63. Philips Z, Claxton K, Palmer S. 2008. The half-life of truth: What are appropriate time horizons for research decisions?. Med. Decis. Making 28:3287–99
    [Google Scholar]
  64. Quinonero-Candela J, Rasmussen CE. 2005. A unifying view of sparse approximate Gaussian process regression. J. Mach. Learn. Res. 6:1939–59
    [Google Scholar]
  65. Raiffa H, Schlaifer R. 1961. Applied Statistical Decision Theory Cambridge, MA: Harvard Univ. Press
    [Google Scholar]
  66. Razavi S, Jakeman A, Saltelli A, Prieur C, Iooss B et al. 2021. The future of sensitivity analysis: an essential discipline for systems modeling and policy support. Environ. Model. Softw. 137:104954
    [Google Scholar]
  67. Reeves BC, Rooshenas L, Macefield RC, Woodward M, Welton NJ et al. 2019. Three wound-dressing strategies to reduce surgical site infection after abdominal surgery: the Bluebelle feasibility study and pilot RCT. Health Technol. Assess. 23:391
    [Google Scholar]
  68. Repo AJ. 1989. The value of information: approaches in economics, accounting, and management science. J. Am. Soc. Inform. Sci. 40:268–85
    [Google Scholar]
  69. Roos M, Martins TG, Held L, Rue H. 2015. Sensitivity analysis for Bayesian hierarchical models. Bayesian Anal 10:2321–49
    [Google Scholar]
  70. Ryan EG, Drovandi CC, McGree JM, Pettitt AN. 2016. A review of modern computational algorithms for Bayesian optimal design. Int. Stat. Rev. 84:1128–54
    [Google Scholar]
  71. 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]
  72. Saltelli A, Chan K, Scott EM, eds. 2000. Sensitivity Analysis New York: Wiley
    [Google Scholar]
  73. Sobol IM. 1993. Sensitivity analysis for non-linear mathematical models. Math. Model. Comput. Exp. 1:407–14
    [Google Scholar]
  74. Spiegelhalter DJ, Abrams KR, Myles JP. 2004. Bayesian Approaches to Clinical Trials and Health-Care Evaluation New York: Wiley
    [Google Scholar]
  75. Stanfill B, Mielenz H, Clifford D, Thorburn P 2015. Simple approach to emulating complex computer models for global sensitivity analysis. Environ. Model. Softw. 74:140–55
    [Google Scholar]
  76. Strong M, Oakley JE. 2013. An efficient method for computing single-parameter partial expected value of perfect information. Med. Decis. Mak. 33:6755–66
    [Google Scholar]
  77. Strong M, Oakley JE. 2014. When is a model good enough? Deriving the expected value of model improvement via specifying internal model discrepancies. SIAM/ASA J. Uncertain. Quantif. 2:1106–25
    [Google Scholar]
  78. Strong M, Oakley JE, 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]
  79. Strong M, Oakley JE, 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]
  80. Thom HHZ, Jackson CH, Welton NJ, Sharples LD. 2017. Using parameter constraints to choose state structures in cost-effectiveness modelling. Pharmacoeconomics 35:9951–62
    [Google Scholar]
  81. Villar SS, Bowden J, Wason J. 2015. Multi-armed bandit models for the optimal design of clinical trials: benefits and challenges. Stat. Sci. 30:2199
    [Google Scholar]
  82. Walker S, Sculpher M, Claxton K, Palmer S. 2012. Coverage with evidence development, only in research, risk sharing, or patient access scheme? A framework for coverage decisions. Value Health 15:3570–79
    [Google Scholar]
  83. Welton N, Ades A, Caldwell D, Peters T 2008. Research prioritization based on expected value of partial perfect information: a case-study on interventions to increase uptake of breast cancer screening. J. R. Stat. Soc. Ser. A 171:4807–41
    [Google Scholar]
  84. Welton N, Strong M, Jackson C, Baio G 2020. Economic evaluation and cost-effectiveness of health care interventions. Bayesian Methods in Pharmaceutical Research E Lesaffre, G Baio, B Boulanger 301–15 Boca Raton, FL: Chapman and Hall/CRC
    [Google Scholar]
  85. Welton NJ, Sutton AJ, Cooper N, Abrams KR, Ades AE. 2012. Evidence Synthesis for Decision Making in Healthcare New York: Wiley
    [Google Scholar]
  86. Willan AR, Pinto EM. 2005. The value of information and optimal clinical trial design. Stat. Med. 24:121791–806
    [Google Scholar]
  87. Wilson ECF. 2015. A practical guide to value of information analysis. Pharmacoeconomics 33:2105–21
    [Google Scholar]
  88. Wood SN. 2017. Generalized Additive Models: An Introduction with R Boca Raton, FL: Chapman and Hall/CRC. , 2nd ed..
    [Google Scholar]
  89. Woods DC, Overstall AM, Adamou M, Waite TW 2017. Bayesian design of experiments for generalized linear models and dimensional analysis with industrial and scientific application. Qual. Eng. 29:191–103
    [Google Scholar]
/content/journals/10.1146/annurev-statistics-040120-010730
Loading
/content/journals/10.1146/annurev-statistics-040120-010730
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error