Computing Expected Value of Partial Sample Information from Probabilistic Sensitivity Analysis Using Linear Regression Metamodeling

Medical Decision Making

Volumn 35, Issue 5, 2015, Pages 584-595

  Jalal, Hawre ^a,b   Goldhaber Fiebert, Jeremy D ^b   Kuntz, Karen M ^c  

^a VA PALO ALTO HEALTH CARE SYSTEM   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c UNIVERSITY OF MINNESOTA   (United States)

Author keywords

Bayesian statistical methods; cost benefit analysis; probabilistic sensitivity analysis; simulation methods; value of information

Indexed keywords

ALGORITHM; COMPUTER SIMULATION; COST BENEFIT ANALYSIS; DECISION TREE; HUMAN; MARKOV CHAIN; PROBABILITY; PROCEDURES; STATISTICAL MODEL;

ALGORITHMS; COMPUTER SIMULATION; COST-BENEFIT ANALYSIS; DECISION TREES; HUMANS; LINEAR MODELS; MARKOV CHAINS; PROBABILITY;

EID: 84931461022     PISSN: 0272989X     EISSN: 1552681X     Source Type: Journal    
DOI: 10.1177/0272989X15578125     Document Type: Article

References (58)

1. Felli J, Hazen G. Sensitivity analysis and the expected value of perfect information. Med Decis Mak. 1998 ; 18 (1). 95-109
2. Felli J, Hazen G. A Bayesian approach to sensitivity analysis. Health Econ. 1999 ; 268 (3). 263-8
3. Koerkamp B, Hunink M, Stijnen T, Weinstein MC. Identifying key parameters in cost-effectiveness analysis using value of information: a comparison of methods. Health Econ. 2006 ; 15 (4). 383-92
4. Claxton K, Posnett J. An economic approach to clinical trial design and research priority-setting. Health Econ. 1996 ; 5 (6). 513-24
5. Coyle D, Grunfeld E, Wells G. The assessment of the economic return from controlled clinical trials: a framework applied to clinical trials of colorectal cancer follow-up. Eur J Health Econ. 2003 ; 4 (1). 6-11
6. Hall PS, McCabe C, Brown JM, Cameron DA. Health economics in drug development: efficient research to inform healthcare funding decisions. Eur J Cancer. 2010 ; 46 (15). 2674-80
7. Briggs A, Sculpher M, Claxton K Decision Modelling for Health Economic Evaluation. New York: Oxford University Press ; 2006 :
8. Claxton K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ. 1999 ; 18 (3). 341-64
9. Kent S, Briggs A, Eckermann S, Berry C. Are value of information methods ready for prime time? An application to alternative treatment strategies for NSTEMI patients. Int J Technol Assess Health Care. 2013 ; 29 (4). 435-42
10. Koerkamp BG, Spronk S, Stijnen T, Hunink MGM. Value of information analyses of economic randomized controlled trials: the treatment of intermittent claudication. Value Health. 2010 ; 13 (2). 242-50
11. Henriksson M, Lundgren F, Carlsson P. Informing the efficient use of health care and health care research resources - the case of screening for abdominal aortic aneurysm in Sweden. Health Econ. 2006 ; 1322 (12). 1311-22
12. Bojke L, Claxton K, Sculpher MJ, Palmer S. Identifying research priorities: the value of information associated with repeat screening for age-related macular degeneration. Med Decis Mak. 2008 ; 28 (1). 33-43
13. Philips Z, Claxton K, Palmer S. Priority setting for research in health care: an application of value of information analysis to glycoprotein IIb/IIIa antagonists in non-ST elevation acute coronary syndrome. Int J Technol Assess Health Care. 2006 ; 22 (3). 379-87
14. Welton N, Ades A. 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. 2008 ; 171 (4). 807-41
15. Willan AR, Eckermann S. Value of information and pricing new healthcare interventions. Pharmacoeconomics. 2012 ; 30 (6). 447-59
16. Eckermann S, Willan ARA. Expected value of information and decision making in HTA. Health Econ. 2007 ; 16 (2). 195-209
17. Claxton KP, Sculpher MJ. Using value of information analysis to prioritise health research: some lessons from recent UK experience. Pharmacoeconomics. 2006 ; 24 (11). 1055-68
18. Hoomans T, Fenwick EL, Palmer S, Claxton K. Value of information and value of implementation: application of an analytic framework to inform resource allocation decisions in metastatic hormone-refractory prostate cancer. Value Health. 2009 ; 12 (2). 315-24
19. Spiegelhalter D, Myles J. Bayesian methods in health technology assessment: a review. Health Technol Assess (Rockv). 2000 ; 4 (38). 1-130
20. Chilcott J, Brennan A, Booth A, Karnon J, Tappenden P. The role of modelling in prioritising and planning clinical trials. Health Technol Assess (Rockv). 2003 ; 7 (23). 1-138
21. Claxton K, Cohen JT, Neumann PJ. When is evidence sufficient?. Health Aff. 2005 ; 24 (1). 93-101
22. Claxton K, Lacey LFL, Walker SGS. Selecting treatments: a decision theoretic approach. J R Stat Soc. 2000 ; 163 (2). 211-225
23. Claxton K, Sculpher M, Drummond M. A rational framework for decision making by the National Institute for Clinical Excellence (NICE). Lancet. 2002 ; 360 (9334). 711-715
24. Fenwick E, Palmer S, Claxton K, Sculpher M, Abrams K, Sutton A. An iterative Bayesian approach to health technology assessment: application to a policy of preoperative optimization for patients undergoing major elective surgery. Med Decis Mak. 2006 ; 26 (5). 480-496
25. Koerkamp BBG, Nikken JJ, Oei EHE, Stijnen T, Ginai AZ, Hunink MGM. Value of information analysis used to determine the necessity of additional research: MR imaging in acute knee trauma as an example. Radiology. 2008 ; 246 (2). 420-5
26. Ades A, Lu G, Claxton K. Expected value of sample information calculations in medical decision modeling. Med Decis Mak. 2004 ; 24 (2). 207-27
27. Myers E, Sanders GD, Ravi D, et al Evaluating the Potential Use of Modeling and Value-of-Information Analysis for Future Research Prioritization Within the Evidence-based Practice Center Program. Rockville, MD: Agency for Healthcare Research and Quality ; 2011 :
28. Myers E, McBroom A, Shen L, Posey R, Gray M Value-of-Information Analysis for Patient-Centered Outcomes Research Prioritization. Washington, DC: Duke Evidence-based Practice Center for Patient-Centered Outcomes Research Institute ; 2012 :
29. Steuten L, van de, Wetering G, Groothuis-Oudshoorn K, Retèl V. A systematic and critical review of the evolving methods and applications of value of information in academia and practice. Pharmacoeconomics. 2013 ; 31 (1). 25-48
30. Brennan A, Kharroubi SA, Chilcott J, O'Hagan A. A two level Monte Carlo approach to calculation expected value of sample information: how to value a research design. Paper presented at the 24th Annual Meeting of the Society for Medical Decision Making ; Baltimore, MD ;..
31. Welton NJ, Madan JJ, Caldwell DM, Peters TJ, Ades AE. Expected value of sample information for multi-arm cluster randomized trials with binary outcomes. Med Decis Making. 2014 ; 34 (3). 352-65
32. Brennan A, Kharroubi S. Expected value of sample information for Weibull survival data. Health Econ. 2007 ; 16 (11). 1205-25
33. Brennan A, Kharroubi S a. Efficient computation of partial expected value of sample information using Bayesian approximation. J Health Econ. 2007 ; 26 (1). 122-48
34. StrongMOakleyJBrennanA. An efficient method for computing the expected value of sample information: a non-parametric regression approach. http://sheffield.ac.uk
35. Raiffa H, Schlaifer R Probability and Statistics for Business Decisions. New York: McGraw-Hill ; 1959 :
36. Raiffa H Decision Analysis: Introductory Lectures on Choices under Uncertainty. Reading, MA: Addison-Wesley; ; 1968 :
37. Schlaifer R Introduction to Statistics for Business Decisions. New York: McGraw-Hill ; 1961 :
38. Claxton K, Sculpher M. Using value of information analysis to prioritise health research. Pharmacoeconomics. 2006 ; 24 (11). 1055-68
39. Willan AR, Pinto EM. The value of information and optimal clinical trial design. Stat Med. 2005 ; 24 (12). 1791-806
40. Coyle D, Oakley J. Estimating the expected value of partial perfect information: a review of methods. Eur J Heal Econ. 2008 ; 9 (3). 251-9
41. Jalal H, Dowd B, Sainfort F, Kuntz KM. Linear regression metamodeling as a tool to summarize and present simulation model results. Med Decis Making. 2013 ; 33 (7). 880-90
42. Heitjan DF. Fieller's method and net health benefits. Health Econ. 2000 ; 9 (4). 327-35
43. Laska EM, Meisner M, Siegel C, Wanderling J. Statistical cost-effectiveness analysis of two treatments based on net health benefits. Stat Med. 2001 ; 20 (8). 1279-302
44. Stinnett A, Mullahy J. Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis. Med Decis Mak. 1998 ; 18 (2). S68-80
45. Briggs A, Weinstein MC, Fenwick EL, Karnon J, Sculpher MJ, Paltiel D. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6. Med Decis Mak. 2012 ; 32 (5). 722-32
46. Doubilet P, Begg CB, Weinstein MC, et al. Probabilistic sensitivity analysis using Monte Carlo simulation: a practical approach. Med Decis Mak. 1985 ; 5 (2). 157
47. Morita S, Thall PF, Müller P. Determining the effective sample size of a parametric prior. Biometrics. 2008 ; 64 (2). 595-602
48. Kleijnen JPC Design and Analysis of Simulation Experiments. New York: Springer ; 2007 :
49. Ades A, Claxton K, Sculpher M. Evidence synthesis, parameter correlation and probabilistic sensitivity analysis. Health Econ. 2006 ; 15 (4). 373-81
50. Jalal H, Kuntz KM. Building correlations among model parameters: a practical approach. Paper presented at the 35th Annual Meeting of the Society for Medical Decision Making ; Baltimore, MD ;..
51. Lau J, Ioannidis JP, Schmid CH. Summing up evidence: one answer is not always enough. Lancet. 1998 ; 351 (9096). 123-7
52. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986 ; 7 (3). 177-88
53. Thompson SG, Smith TC, Sharp SJ. Investigating underlying risk as a source of heterogeneity in meta-analysis. Stat Med. 1997 ; 16 (23). 2741-58
54. Thompson SG, Sharp SJ. Explaining heterogeneity in meta-analysis: a comparison of methods. Stat Med. 1999 ; 18 (20). 2693-708
55. Thompson SG. Why sources of heterogeneity in meta-analysis should be investigated. Br Med J. 1994 ; 309 (6965). 1351-5
56. Ades AE. A chain of evidence with mixed comparisons: models for multi-parameter synthesis and consistency of evidence. Stat Med. 2003 ; 22 (19). 2995-3016
57. Willan AR, Eckermann S. Accounting for between-study variation in incremental net benefit in value of information. Health Econ. 2012 ; 21 (10). 1183-95
58. Tappenden P, Eggington S, Oakley J, Mccabe C. Methods for expected value of information analysis in complex health economic models. Health Technol Assess (Rockv). 2004 ; 8 (27). 1-78