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Cost-effectiveness analyses of genetic and genomic diagnostic tests

  • Nature Reviews Genetics volume 19, pages 235246 (2018)
  • doi:10.1038/nrg.2017.108
  • Download Citation


Developments in next-generation sequencing technologies have driven the clinical application of diagnostic tests that interrogate the whole genome, which offer the chance to diagnose rare inherited diseases or inform the targeting of therapies. New genomic diagnostic tests compete with traditional approaches to diagnosis, including the genetic testing of single genes and other clinical strategies, for finite health-care budgets. In this context, decision analytic model-based cost-effectiveness analysis is a useful method to help evaluate the costs versus consequences of introducing new health-care interventions. This Perspective presents key methodological, technical, practical and organizational challenges that must be considered by decision-makers responsible for the allocation of health-care resources to obtain robust and timely information about the relative cost-effectiveness of the increasing numbers of emerging genomic tests.

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  1. 1.

    & Integrated regional genetic services: current and future provision. BMJ 322, 1048 (2001).

  2. 2.

    et al. Provision of genetic services in Europe: current practices and issues. Eur. J. Hum. Genet. 11 (Suppl. 2), S13–S48 (2003).

  3. 3.

    & Genomics research: world survey of public funding. BMC Genomics 10, 472 (2008).

  4. 4.

    [No authors listed.] The NHGRI Genome Sequencing Program (GSP). NIH National Human Genome Reasearch Institute (2016).

  5. 5.

    [No authors listed.] AstraZeneca launches integrated genomics approach to transform drug discovery and development. AstraZeneca (2016).

  6. 6.

    [No authors listed.] 2012 Large-Scale Applied Research Project Competition — Genomics and Personalized Health. Genome Canada (2012).

  7. 7.

    [No authors listed.] Request for Applications — 2017 Large-Scale Applied Research Project Competition: Genomics and Precision Health. Genome Canada (2017).

  8. 8.

    Aviesan. France Médecine Génomique 2025. (2016).

  9. 9.

    [No authors listed.] The 100,000 Genomes Project. Genomics England (2016).

  10. 10.

    et al. The sequence of the human genome. Science 40, 1304 (2001).

  11. 11.

    Evolving healthcare through personal genomics. Nat. Rev. Genet. 18, 259–267 (2017).

  12. 12.

    et al. Trouble with ataxia: a longitudinal qualitative study of the diagnosis and medical management of a group of rare, progressive neurological conditions. SAGE Open Med. 1, 2050312113505560 (2013).

  13. 13.

    , , , & Using cost-effectiveness analysis to quantify the value of genomic-based diagnostic tests: recommendations for practice and research. Genet. Test. Mol. Biomarkers 21, 705–716 (2017).

  14. 14.

    [No authors listed.] The cost of sequencing a human genome. NIH National Human Genome Reasearch Institute (2016).

  15. 15.

    et al. Annual Report of the Chief Medical Officer 2016 — Generation Genome (Department of Health, 2017).

  16. 16.

    The race for the $1000 genome. Science 311, 1544–1546 (2006).

  17. 17.

    The $1,000 genome, the $100,000 analysis? Genome Med. 2, 84 (2010).

  18. 18.

    , & Is the “$1000 Genome” really $1000? Understanding the full benefits and costs of genomic sequencing. Technol. Health Care 23, 373–379 (2015).

  19. 19.

    et al. The cost and cost trajectory of whole-genome analysis guiding treatment of patients with advanced cancers. Mol. Genet. Genom. Med. 5, 251–260 (2017).

  20. 20.

    et al. Budget impact analysis — Principles of good practice: report of the ISPOR 2012 Budget Impact Analysis Good Practice II Task Force. Value Health 17, 5–14 (2014).

  21. 21.

    , , & Towards health technology assessment of whole genome sequencing: challenges and solutions. Pers. Med. (2017).

  22. 22.

    [No authors listed.] Spotlight on specialised services. NHS England (2017).

  23. 23.

    , , & The budget impact and cost-effectiveness of introducing whole-exome sequencing-based virtual gene panel tests into routine clinical genetics. (PHG Foundation, 2017).

  24. 24.

    Fish and chips all round? Regulation of genetic-based technologies. Health Econom. 18, 1233–1236 (2009).

  25. 25.

    , , , & Methods for the Economic Evaluation of Health Care Programmes 3rd edn (Oxford Univ. Press, 2005).

  26. 26.

    [No authors listed.] Genetic test evaluation. UK Genetic Testing Network (2017).

  27. 27.

    [No authors listed.] ACCE Model Process for Evaluating Genetic Tests. Centers for Disease Control and Prevention (2010).

  28. 28.

    [No authors listed.] Guide to the Methods of Technology Appraisal 2013 (National Institute for Health and Care Excellence, 2013).

  29. 29.

    [No authors listed.] Diagnostic Assessment Programme Manual (National Institute for Health and Care Excellence, 2011).

  30. 30.

    , , & A social tariff for EuroQol: results from a UK general population survey. University of York Discussion Paper, 138 (1995).

  31. 31.

    & Net health benefits: a new framework for the analysis of uncertainty in cost-effectiveness analysis. Med. Decision Making 18 (Suppl. 2), S68–S80 (1998).

  32. 32.

    Construction of the contingent valuation market in health care: a critical assessment. Health Econ. 12, 609–628 (2003).

  33. 33.

    [No authors listed.] The Green Book: Appraisal and Evaluation in Central Government (HM Treasury, 2003)

  34. 34.

    & Contingent valuation: what needs to be done? Health Econom. Policy Law 5, 91–111 (2010).

  35. 35.

    & The death of cost-minimization analysis? Health Econ. 10, 179–184 (2001).

  36. 36.

    & Cost-minimisation analysis versus cost-effectiveness analysis, revisited. Health Econ. 22, 22–34 (2013).

  37. 37.

    Is economic evaluation in touch with society's health values? Br. Med. J. 329, 1233–1236 (2004).

  38. 38.

    , & Health economics and cost consequences analysis: a step back in time. BMJ Rapid Response (2005).

  39. 39.

    , & Cost consequences: implicit, opaque and anti scientific. BMJ Rapid Response (2005).

  40. 40.

    The normative economics of health care finance and provision. Oxford Rev. Econom. Policy 5, 34–58 (1989).

  41. 41.

    & Mark Pauly on welfare economics: Normative rabbits from positive hats. J. Health Econ. 15, 243–251 (1996).

  42. 42.

    in Handbook of Health Economics Vol. 1 (eds Culyer, A. J. & Newhouse, J. P.) 55–118 (2000).

  43. 43.

    & Economics of pharmacogenomics: rethinking beyond QALYs? Pharmacogenomics 11, 187–195 (2013).

  44. 44.

    , , & Welfarism versus extra-welfarism. J. Health Econ. 27, 325–338 (2008).

  45. 45.

    [No authors listed.] Pharmacoeconomic Guidelines around the World. International Society for Pharmacoeconomics and Outcome Research (ISPOR) (2017).

  46. 46.

    , , & Whither trial-based economic evaluation for health care decision making? Health Econ. 15, 677–687 (2006).

  47. 47.

    , , & Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med. Decision Making 33, 607–617 (2013).

  48. 48.

    , & Decision Modelling for Health Economic Evaluation (Oxford Univ. Press, 2006).

  49. 49.

    , Schnell-Inderst, Appropriate evidence sources for populating decision analytic models within health technology assessment (HTA). A systematic review of HTA manuals and health economic guidelines. Med. Decision Making 34, 288–299 (2014).

  50. 50.

    , , , & Good practice guidelines for decision analytic modelling in health technology assessment: a review and consolidation of quality assessment. Pharmacoeconomics 24, 355–371 (2006).

  51. 51.

    Exploring uncertainty in cost-effectiveness analysis. Pharmacoeconomics 26, 781–798 (2008).

  52. 52.

    et al. Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra. Health Econ. 14, 339–347 (2005).

  53. 53.

    & Modelling in health economic evaluation. What is its place? What is its value? Pharmacoeconomics 17, 445–459 (2000).

  54. 54.

    et al. Modelling in economic evaluation: an unavoidable fact of life. Health Econ. 6, 217–227 (1997).

  55. 55.

    et al. A pragmatic randomised controlled trial of thiopurine methyltransferase (TPMT) genotyping in the management of patients, prior to azathioprine treatment: The TARGET study. Pharmacogenomics 12, 815–826 (2011).

  56. 56.

    , , , & The cost-effectiveness of a pharmacogenetic test: a trial-based evaluation of TPMT testing for azathioprine. Value Health 17, 22–33 (2014).

  57. 57.

    et al. Conceptualizing a model: a report of the ISPOR-SMDM modeling good research practices task force-2. Value Health 15, 804–811 (2012).

  58. 58.

    , & A taxonomy of model structures for economic evaluation of health technologies. Health Econom. 15, 1295–1310 (2006).

  59. 59.

    et al. State-transition modeling: a report of the ISPOR-SMDM modeling good research practices task force-3. Value Health 15, 812–820 (2012).

  60. 60.

    et al. Modeling using discrete event simulation: a report of the ISPOR-SMDM modeling good research practices task force-4. Value Health 15, 821–827 (2012).

  61. 61.

    et al. Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices task force-7. Value Health 15, 843–850 (2012).

  62. 62.

    et al. Model parameter estimation and uncertainty: a report of the ISPOR-SMDM modelling good research practices task force-6. Value Health 15, 835–842 (2012).

  63. 63.

    , & Representing uncertainty: the role of cost-effectiveness acceptability curves. Health Econom. 10, 779–787 (2001).

  64. 64.

    & Implications of spillover effects within the family for medical cost-effectiveness analysis. J. Health Econ. 24, 751–773 (2005).

  65. 65.

    [No authors listed.] EGFR-TK Mutation Testing in Adults With Locally Advanced or Metastatic Non-Small-Cell Lung Cancer. (National Institute for Health and Care Excellence, 2013).

  66. 66.

    , & Economic evaluations of personalized medicine: existing challenges and current developments. Pharmacogenom. Personalized Med. 8, 115–126 (2015).

  67. 67.

    , & Current methodological issues in the economic assessment of personalized medicine. Value Health 16 (Suppl. 6), S20–S26 (2013).

  68. 68.

    , & Issues surrounding the health economic evaluation of genomic technologies. Pharmacogenomics 14, 1833–1847 (2013).

  69. 69.

    et al. Concepts of 'personalization' in personalized medicine: implications for economic evaluation. Pharmacoeconomics 33, 49–59 (2015).

  70. 70.

    , , & Economic perspectives on personalized health care and prevention. Forum Health Econ. Policy (2013).

  71. 71.

    & Welfarism versus extra-welfarism: can the choice of economic evaluation approach impact on the adoption decisions recommended by economic evaluation studies? Pharmacoeconomics 33, 571–579 (2015).

  72. 72.

    ACMG Board of Directors. Clinical utility of genetic and genomic services: a position statement of the American College of Medical Genetics and Genomics. Genet. Med. 17, 505–507 (2005).

  73. 73.

    & What is the clinical utility of genetic testing? Genet. Med. 8, 448–450 (2006).

  74. 74.

    , & Evaluating the utility of personal genomic information. Genet. Med. 11, 570–574 (2009).

  75. 75.

    , & Genetic testing: clinical and personal utility. Virtual Mentor 14, 604–609 (2012).

  76. 76.

    , & Valuing the economic benefits of complex interventions: when maximising health status is not sufficient. Health Econom. 22, 258–271 (2013).

  77. 77.

    , & Welfarism, extra-welfarism and capability: the spread of ideas in health economics. Social Sci. Med. 67, 1190–1198 (2008).

  78. 78.

    [No authors listed.] ICECAP capability measures. University of Birmingham (2017).

  79. 79.

    [No authors listed.] The Social Care Guidance Manual (National Institute for Health and Care Excellence, 2016).

  80. 80.

    , , , & Are quality-adjusted life years a good proxy measure of individual capabilities? Pharmacoeconomics 35, 637–646 (2017).

  81. 81.

    , , & Assessing sufficient capability: a new approach to economic evaluation. Social Sci. Med. 139, 71–79 (2015).

  82. 82.

    & Value of information on preference heterogeneity and individualized care. Med. Decis. Making 27, 112–117 (2007).

  83. 83.

    , , & The value of heterogeneity for cost-effectiveness subgroup analysis: conceptual framework and application. Med. Decis. Making 34, 951–964 (2014).

  84. 84.

    & Markov models in medical decision making: a practical guide. Med. Decis. Making 13, 322–338 (1993).

  85. 85.

    & An introduction to Markov modelling for economic evaluation. Pharmacoeconomics 13, 397–409 (1998).

  86. 86.

    & Using mathematical optimisation in model-based cost-effectiveness analyses: a case study of a stratified breast screening programme. Value Health 20, A751–A752 (2017).

  87. 87.

    , , & Introduction to Algorithms (The MIT Press, 2009).

  88. 88.

    et al. Cost-effectiveness and harm-benefit analyses of risk-based screening strategies for breast cancer. PLoS ONE 9, e86858 (2014).

  89. 89.

    et al. Evaluation of a national stratified breast screening programme in the United Kingdom: a model-based cost-effectiveness analysis. Value Health 20, 1100–1109 (2017).

  90. 90.

    [No authors listed.] Retinal degeneration 105 gene panel. UK Genetic Testing Network (2017).

  91. 91.

    , , , & Discrete Event Simulation for Health Technology Assessment (Chapman and Hall/CRC, 2015).

  92. 92.

    , , & Discrete event simulation-based resource modelling in health technology assessment. Pharmacoeconomics 35, 989–1006 (2017).

  93. 93.

    Essays on the Theory of Constraints (North River Press, 1998).

  94. 94.

    Model Building in Mathematical Programming (John Wiley & Sons, 1999).

  95. 95.

    & Introduction to Operations Research (McGraw-Hill, 2010).

  96. 96.

    et al. Systems for grading the quality of evidence and the strength of recommendations I: Critical appraisal of existing approaches. BMC Health Serv. Res. 4, 38 (2004).

  97. 97.

    et al. Genome sequencing: a systematic review of health economic evidence. Health Econom. Rev. 3, 29 (2013).

  98. 98.

    et al. A microcosting study of immunogenicity and TNFi drug level tests for therapeutic drug monitoring in clinical practice. Rheumatology 55, 2131–2137 (2016).

  99. 99.

    et al. Bill and Melinda Gates Foundation Methods for Economic Evaluation Project (MEEP). Final Report and Appendices (National Institute for Health and Care Excellence, 2014).

  100. 100.

    [No authors listed.] Bill and Melinda Gates Foundation Methods for Economic Evaluation Project (MEEP). The Gates Reference Case: What it is, why it's important, and how to use it (National Institute for Health and Care Excellence, 2014).

  101. 101.

    US National Library of Medicine. What is precision medicine? Genetics Home Reference (2017).

  102. 102.

    Health Technology Assessment of Next-Generation Sequencing. Thesis, Raboud University (2017).

  103. 103.

    et al. Cost-effectiveness of massively parallel sequencing for diagnosis of paediatric muscle diseases. Genom. Med. 2, 4 (2017).

  104. 104.

    et al. Prospective comparison of the cost-effectiveness of clinical whole-exome sequencing with that of usual care overwhelmingly supports early use and reimbursement. Genet. Med. 19, 867–874 (2017).

  105. 105.

    et al. Using genomic information to guide ibrutinib treatment decisions in chronic lymphocytic leukaemia: a cost-effectiveness analysis. Pharmacoeconomics 35, 845–858 (2017).

  106. 106.

    et al. Cost-effectiveness of precision medicine in the fourth-line treatment of metastatic lung adenocarcinoma: An early decision analytic model of multiplex targeted sequencing. Lung Cancer 107, 22–35 (2017).

  107. 107.

    et al. The cost-effectiveness of returning incidental findings from next-generation genomic sequencing. Genet. Med. 17, 587–595 (2015).

  108. 108.

    Personalized medicine in diabetes: the role of 'omics' and biomarkers. Diabet Med. 33, 712–717 (2016).

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The authors acknowledge the help of H. Moore with the systematic review of published economic evaluations of precision medicine in Supplementary information S1. The authors also acknowledge the expert advice on the applications of genomic tests from G. Black, G. Burghel, G. Hall and B. Newman. K.P. has a research programme supported by grants awarded from the National Institute for Health Research, the Medical Research Council (MRC), the Engineering and Physical Sciences Research Council (EPSRC), Lupus UK and The Swedish Foundation for Humanities and Social Sciences. S.W. is funded by a Wellcome Trust PhD Studentship. A.J.T. is funded by a grant awarded to The University of Manchester for the Manchester Molecular Pathology and Innovation Centre (MMPathIC) by the MRC and the EPSRC. S.P.G. is supported by two grants awarded to The University of Manchester for MASTERPLANS funded by the MRC (grant number MR/M01665X/1) Stratified Medicine Programme and by Lupus UK. The views expressed in this article are those of the authors and not the funding bodies.

Author information


  1. Manchester Centre for Health Economics, Division of Population Health, Health Services Research and Primary Care, The University of Manchester, Manchester, M13 9PL, UK.

    • Katherine Payne
    • , Sean P. Gavan
    • , Stuart J. Wright
    •  & Alexander J. Thompson


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The authors contributed equally to all aspects of this manuscript.

Competing interests

All authors are currently involved with publicly funded research programmes that use economic methods to evaluate the impact of examples of precision medicine and that occasionally include the use of genomics or genetic tests.

Corresponding author

Correspondence to Katherine Payne.

Supplementary information

PDF files

  1. 1.

    Supplementary information S1

    Supplementary Information for: Cost-effectiveness analyses of genetic and genomic-targeted diagnostic technologies


Budget impact

The total budget impact of a new technology in terms of the cost falling on the budget holder. Budget impact should be calculated by considering the perspective of the specific health-care decision-maker, the size and characteristics of the population, the current and new treatment mix, the effectiveness and safety of the new and current treatments, the resource use and costs for the treatments and symptoms as they would apply to the population of interest.

Companion diagnostics

Also referred to as the test in a test-and-treat strategy. Diagnostic tests (typically an in vitro diagnostic) co-developed alongside a pharmaceutical agent and stated explicitly within the product label as it is essential for the safe and effective use of the corresponding medicine.

Cost-benefit analysis

(CBA). A type of economic evaluation that compares the relative costs and consequences of different courses of action in which the consequences are measured using an approach that captures the impact in terms of money, such as willingness to pay.

Cost-consequences analysis

(CCA). A type of economic evaluation that compares the relative costs and consequences of different courses of action in which the consequences are measured using different outcomes and presented separately.

Cost-effectiveness analysis

(CEA). A type of economic evaluation that compares the relative costs and consequences of different courses of action, in which the consequences are measured using outcomes that capture the impact on clinical effectiveness. Often used synonymously with cost-utility analysis, in which the consequences are measured using quality-adjusted life-years.

Cost-effectiveness threshold

The additional cost that must be imposed on the budget for health care to displace one quality-adjusted life-year (QALY) elsewhere within the health-care system. Interventions that increase cost but with an incremental cost per QALY below the threshold are typically viewed as being cost-effective. NICE (National Institute for Health Care Excellence) uses a threshold range thought to be between £20,000 and £30,000 per QALY gained.

Cost-minimization analysis

(CMA). A type of economic evaluation that compares only the relative costs of different courses of action as it is assumed that the consequences are equal.

Decision analytic model

A series of mathematical relationships that represent the progression of a patient's diagnosis or disease and the impact of a health technology on diagnosis and/or disease progression. The output of a decision analytic model can be expressed in terms of the expected outcomes of interest for each alternative comparator strategy.

Decision problem

An explicit statement of the resource allocation decision under consideration.

Decision trees

A decision analytic modelling technique that simulates a cohort of patients following a predefined pathway with associated probabilities, costs and outcomes. Decision trees do not typically incorporate a time component.

Discrete event simulation

(DES). A decision analytic modelling technique that simulates the histories of individual patients over time, characterized by the specific events that they may experience.


A set of normative principles that guides the conduct, design and interpretation of an economic evaluation. Extra-welfarism is typically taken to underpin the use of cost-effectiveness analysis as the method of economic evaluation and the quality-adjusted life-year as the metric of benefit and/or outcome.

Incremental costs

The difference in cost between two alternative interventions.

Incremental net benefit

(INB). Can be measured in monetary units (incremental net monetary benefit) or units of health gain (incremental net health benefit). If measured in monetary units (for example, dollars or euros), the monetary difference between expected net benefit of the new intervention and the expected net benefit of the relevant comparator.

National Institute for Health and Care Excellence

(NICE). The decision-making authority responsible for making recommendations regarding the allocation of population health-care resources in England.

Opportunity cost

The benefit forgone from the next best use of a specific resource. The opportunity cost of resource allocation decisions for health care can be expressed in the health benefits forgone.

Probabilistic sensitivity analysis

A form of sensitivity analysis where uncertainty is propagated through the characterization of input parameters as probability distributions and the sampling of values for parameters using Monte Carlo simulation

Quality-adjusted life-years

(QALYs). A generic outcome measure of health benefit calculated by multiplying each year of life by a weight that represents its health-related quality of life. Weights are calculated according to the reference points of one (full health) and zero (death); states worse than death are possible.

Reference case

A prespecified preferred set of criteria for conducting an economic evaluation. A reference case is typically an expression of a decision-maker's value judgement.

State transition Markov models

A type of decision analytic model that conceptualizes a problem by defining relevant health states through which a cohort of patients transitions over time.

Study perspective

The scope of the costs that should be included in an economic evaluation. The perspective is typically defined by the budget constraint of the decision-maker. Examples include a health-care system perspective and a societal perspective. The perspective also helps to determine the relevant outcome chosen for analysis.

Time horizon

The scope of the costs and consequences that should be included in an economic evaluation, from the present until a defined point in the future. The time horizon for a study should be sufficient to allow all relevant costs and consequences to be incorporated, which, in general, requires a lifetime time horizon to be used. The lifetime is taken to be that of the last dying patient within the analysis cohort.


A key component of any economic evaluation. There are different types of uncertainty, such as parameter, methodological and decision. The impact of parameter and methodological uncertainty can be captured using sensitivity analysis, such as probabilistic sensitivity analysis or scenario analysis, respectively. Decision uncertainty is the probability that an incorrect decision is made in the context of resource allocation decisions for health care.


A set of normative principles that guides the conduct, design and interpretation of an economic evaluation. Welfarism places individual utilities at the heart of the evaluative space and is typically taken to be consistent with the use of cost-benefit analysis and the use of willingness to pay as the metric of benefit and/or outcome.