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Cost-effectiveness analyses of genetic and genomic diagnostic tests

Nature Reviews Genetics volume 19, pages 235246 (2018) | 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.

Key points

  • Informed by public and privately funded programmes, advances in next-generation sequencing technologies have yielded genomic-targeted diagnostic tests based on either multigene panels, whole-exome sequencing or whole-genome sequencing, which are starting to replace some genetic tests in clinical practice.

  • Cost-effectiveness analysis (CEA) of new genomic tests that explicitly quantifies the costs and consequences of the new test compared with other potential uses of a health-care budget can provide decision-makers with information to guide resource allocation decisions.

  • Many challenges exist when estimating the magnitude of the budget impact of genetic and genomic tests and services. The existing literature has focused on the downward trend in the cost of sequencing, but estimating the opportunity cost (by considering both the cost and consequences) of introducing new genomic tests is vitally important.

  • To inform decisions about the efficient allocation of health-care resources, model-based CEAs must be supported and populated by robust studies, which ideally span the three component parts of a genomic test: the technology; the diagnostic component and the model of service delivery to provide the diagnostic test; and, if appropriate, subsequent treatment options.

  • An emerging economic evidence base for genetic tests has been identified. It is now time to direct funding to support the empirical research needed to develop the use of decision analytic model-based CEAs of genomic tests, while being cognizant of the known methodological, technical, practical and organizational challenges, to maximize the potential benefits to patient populations.

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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

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  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.

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