Economic Evaluation of Companion and Complementary Diagnostics

C H A P T E R

20 Economic Evaluation of Companion and Complementary Diagnostics Brett Doble1,2 1

Nuffield Department of Population Health, Health Economics Research Centre, University of Oxford, Oxford, United Kingdom 2Programme in Health Services and Systems Research, Duke-NUS Medical School, Singapore, Singapore O U T L I N E 20.1 Economic Evaluation in Health 20.1.1 Cost-Effectiveness Analysis 20.1.2 Cost Utility Analysis 20.1.3 Cost-Minimization Analysis 20.1.4 Cost Benefit Analysis

382 382 382 383 383

20.2 Methods Used to Conduct an Economic Evaluation

384

20.3 Economic Evaluation of Companion Diagnostics 385 20.3.1 Important Considerations in the Economic Evaluation of Companion Diagnostics 385

Companion and Complementary Diagnostics DOI: https://doi.org/10.1016/B978-0-12-813539-6.00020-1

20.3.2 Approaches Used in the Economic Evaluation of Companion Diagnostics 387 20.3.3 Economic Evaluation of Multiple Companion Diagnostics Used in Combination 390 20.3.4 Alternative Approaches to the Economic Evaluation of Companion Diagnostics 391 20.3.5 Expanding the Value Framework for Companion Diagnostics 393 20.4 Summary

395

References

395

381

© 2019 Elsevier Inc. All rights reserved.

382

20. ECONOMIC EVALUATION OF COMPANION AND COMPLEMENTARY DIAGNOSTICS

20.1 ECONOMIC EVALUATION IN HEALTH In the provision of health care, decisions need to be made concerning different uses of scarce resources. Influencing these decisions is our understanding of the relative costs and consequences of investing in alternative courses of action. Economic evaluation is a mechanism for assembling this information to inform such decisions and is commonly used by health-care providers when considering the adoption/funding of health-care interventions (e.g., drugs, medical devices, diagnostic tests or programs). The formal definition of an economic evaluation is a “comparative analysis of alternative courses of action in terms of both their costs and consequences” [1]. There are four main types of economic evaluation: 1. 2. 3. 4.

cost-effectiveness analysis (CEA), cost-utility analysis (CUA), cost-minimization analysis (CMA), and cost benefit analysis (CBA).

The four types all share a commonality, in that they assess costs in monetary units (e.g., dollars, pounds, euros). They differ, however, in how consequences (hereafter referred to as outcomes) are identified, measured, and valued. These differences are highlighted below along with the resulting strengths and limitations of each type of evaluation for informing adoption/funding decisions of health-care interventions.

20.1.1 Cost-Effectiveness Analysis CEA identifies a single outcome, common to competing interventions (potentially achieved to different degrees), which is measured in some “natural” or clinical unit, for example, symptom-free days, cases prevented, or life-years gained [1]. The outcome used will differ by disease area and is selected to be sensitive to relevant changes in health for a particular condition. For example, life-years gained would be an appropriate outcome if the interventions being considered extend survival (e.g., different drugs to treat lung cancer). For interventions that only improve symptoms and have no impact on survival (e.g., drugs to control asthma), symptom-free days may be a more appropriate outcome. However, in all disease areas, cost-effectiveness is ultimately assessed by comparing the incremental costs and outcomes (in whatever natural unit has been selected) of two or more competing interventions. To identify the most cost-effective intervention, a decision will need to be made as to how much more a decision maker is willing to pay to obtain an additional unit of outcome. Overall, CEA is useful when comparing competing interventions within a single disease, but it can be problematic to infer what interventions to adopt/fund across multiple disease areas as the outcomes used in each CEA may be measured in different units.

20.1.2 Cost Utility Analysis CUA is a subset of CEA where single or multiple outcomes are identified, which may or may not be common between competing interventions [1]. The outcomes are combined

COMPANION AND COMPLEMENTARY DIAGNOSTICS

20.1 ECONOMIC EVALUATION IN HEALTH

383

into a single metric, typically the quality-adjusted life-year (QALY), that captures both the quantity (duration) and quality of life. The QALY is used to convert a year spent in a particular health state into an equivalent number of years in full or perfect health [2]. This conversion is done through the use of a “utility” value (measures quality of life, usually on a scale of 0 to 1), which is derived from individuals’ preferences for particular health states. These preferences are elicited either directly, using approaches, such as time-tradeoff [3] or standard gamble [4], or elicited indirectly via completion of generic or diseasespecific quality-of-life questionnaires (e.g., EQ-5D [5] and EORTC-8D [6], respectively), with responses from the questionnaires matched to previously elicited utility values [6,7]. Similar to CEA, cost-effectiveness using CUA is assessed by comparing the incremental costs and outcomes (expressed as QALYs) of two or more competing interventions. Again, this necessitates a decision as to how much more a decision maker is willing to pay to obtain an additional QALY, in order to identify the most cost-effective intervention. The main benefit of CUA over CEA is that since outcomes are measured in the same units (i.e., QALYs) across different disease areas, it becomes much easier to infer what interventions to adopt/fund. This can simply be done by directly comparing the cost per QALY gained for different interventions used in different disease areas.

20.1.3 Cost-Minimization Analysis CMA is where the outcomes of two or more competing interventions are considered to be equivalent (e.g., a trial of two interventions showing no significant difference in the primary outcome) [2]. The least costly intervention can then be selected as the preferred intervention. However, such an approach is largely inappropriate given the apparent nondifference in outcomes is based on hypothesis testing that has considered outcomes separately from costs [8,9]. The difference in the joint distribution of costs and outcomes is of primary interest when selecting between competing interventions. This joint difference could potentially indicate one intervention being favored over the other even when the interventions are considered equivalent in terms of outcomes [2]. For this reason, CMA is rarely an appropriate type of economic evaluation to employ in practice.

20.1.4 Cost Benefit Analysis In CBA, outcomes are measured using the same metric as costs (i.e., monetary units). Outcomes, including both health (e.g., exacerbations avoided) and nonhealth outcomes (e.g., the “value of knowing;” see Section 20.3.5) associated with an intervention, are valued in monetary terms using methods, such as contingent valuation (e.g., willingness-topay) [10] or conjoint analysis (e.g., discrete choice experiments) [11]. A decision to adopt/ fund an intervention can then simply be made if the outcomes (expressed in monetary units) are greater than the associated costs. There are, however, a number of limitations with CBA, the most prominent being the difficulty in measuring outcomes in monetary units [12], which has limited the use of CBA in informing adoption/funding decisions for health-care interventions.

COMPANION AND COMPLEMENTARY DIAGNOSTICS

384

20. ECONOMIC EVALUATION OF COMPANION AND COMPLEMENTARY DIAGNOSTICS

As the practical application of economic evaluation is mainly limited to CEA and CUA, this chapter will mainly focus on the discussion of issues and application of these two types of economic evaluation as they relate to CDx.

20.2 METHODS USED TO CONDUCT AN ECONOMIC EVALUATION There are two main methods used to conduct CEA and CUA of health-care interventions: decision analytic modeling [13] and trial-based economic evaluations [14]. Both methods present the results of an analysis as an incremental cost-effectiveness ratio: (ICER) (i.e., the average difference in cost of two interventions divided by the average difference in effectiveness of the two interventions). The two methods, however, mainly differ in the sources of evidence used to conduct the analysis. Decision analytic modeling is used to synthesize clinical and economic data from a variety of sources. Generally, the data inputs used to populate a model are based on average estimates from a generalizable population for which an adoption/funding decision will be made, as this is the evidence in the published literature. However, it is also common for decision analytic models to incorporate patient-level data obtained from clinical trials, observational studies, or routinely collected administrative health-care databases [13]. Decision analytic modeling has the advantage of being able to assess all relevant interventions for a decision problem, consider all the evidence available for the competing interventions, and determine the impacts of interventions over a long period of time [15]. Given their flexibility, it is not surprising that decision analytic modeling has become increasingly common to assess the cost-effectiveness of health-care interventions and there are a number of best practice guidelines available [16 20]. These guidelines, however, apply to the evaluation of health-care interventions using decision analytic modeling generally and do not provide specific advice for the economic evaluation of CDx. Trial-based economic evaluations use estimates of costs, resource use, clinical responses, and quality-of-life that are collected for each patient within a randomized controlled trial to assess the cost-effectiveness of the interventions under study. However, owing to the short follow-up of most trials, extrapolations using modeling are usually required to understand the long-term impacts of competing interventions. This generally necessitates the use of additional evidence from the published literature or the application of data from other evidence sources as described for decision analytic models. Trial-based economic evaluations of CDx are, however, very rare in the literature. This is despite a number of trial designs being available to evaluate CDx and accompanying treatments (e.g., biomarker-stratified and double-randomized controlled trial designs) [21]. The lack of trial-based economic evaluations of CDx is more readily explained by the lack of randomized controlled trials for medical tests, more generally [22]. Therefore, the focus of this chapter will mainly relate to the economic evaluation of CDx using decision analytic modeling. Readers interested in conducting trial-based economic evaluations of CDx are referred to an exemplar of such an analysis by Thompson et al. [23] that compares the cost-effectiveness of thiopurine-methyl transferase testing before azathioprine treatment to current practice.

COMPANION AND COMPLEMENTARY DIAGNOSTICS

20.3 ECONOMIC EVALUATION OF COMPANION DIAGNOSTICS

385

20.3 ECONOMIC EVALUATION OF COMPANION DIAGNOSTICS The application of CDx has been shown to lead to improved health outcomes in a number of patient populations as well as result in patients avoiding unsafe and/or ineffective treatments [24 27]. What is not completely understood is the costs that are incurred to achieve these outcomes. Contrary to the assumption that application of CDx will lead to a reduction in health-care costs [28 30], it is likely that under most scenarios the use of CDx will lead to an increase in health-care costs [31]. This even includes the case when CDx enable individuals to avoid treatments that might be ineffective and/or harmful. Ultimately, this increase in health-care costs will be a result of the profit-maximizing behavior of CDx manufacturers. In that, the price set for a CDx will more than counterbalance any savings observed elsewhere in the health-care system, such as treatment costs avoided or a reduction in hospitalizations due to adverse or disease-related events. It is therefore important to ensure that the health outcomes derived from the application of CDx are actually worth the costs incurred. The use of economic evaluation (CEA and CUA) via decision analytic modeling can help decision makers understand if a CDx or combination of CDx should be used in practice by comparing alternative testing strategies in terms of their relative costs and impacts on health outcomes [32].

20.3.1 Important Considerations in the Economic Evaluation of Companion Diagnostics Economic evaluation of CDx can be challenging as CDx usually have an indirect impact on health outcomes (i.e., they result in downstream consequences that are usually dependent on other clinical interventions), meaning that the evaluation of a CDx is usually intrinsically linked to another clinical intervention [33], making it difficult to separate the value of the CDx and that of the linked intervention [34]. Due to this linkage, economic evaluations of CDx require evidence of health outcomes in multiple subgroups of a patient population, which may be impossible or difficult to collect (e.g., health outcomes associated with patients who test as false-negatives or false-positives). In some cases, it may not be clear what the subsequent clinical pathways (i.e., additional testing and/or most appropriate treatment) are for patients that are not selected for treatment when using a CDx. Making it very difficult to determine the costs and health outcomes associated with these patients, which are necessary when conducting an economic evaluation of a CDx. Application of CDx can also change the case mix of the patients who eventually receive treatment and evidence supporting the impact of treatment on health outcomes may have been developed in a different case mix of patients. In this scenario, there may not be appropriate clinical evidence of treatment effectiveness that can be used to support the economic evaluation of a CDx [35]. Given these challenges, evidence requirements, in terms of outcomes to be measured and the most appropriate study types to collect the required information, have not been clearly delineated [36], which is further complicated by the fact that requirements will likely vary depending on the type of CDx, patients to be tested, and other contextual factors. However, a number of recommendations for the evaluation of CDx have been

COMPANION AND COMPLEMENTARY DIAGNOSTICS

386

20. ECONOMIC EVALUATION OF COMPANION AND COMPLEMENTARY DIAGNOSTICS

suggested [33,37] and more recently some health technology assessment agencies that assess CDx for reimbursement have developed guidelines for their evaluation [38 41]. When conducting an economic evaluation of a CDx, all the ways in which it affects health outcomes and costs should be quantified. This will include consideration of outcomes specifically related to the CDx, such as test failure rate, time to receiving the test results, and proportion of inconclusive tests. Evaluations will also need to capture downstream consequences of the CDx, including clinical decisions in terms of further tests, treatment and management of patients, associated health service utilization/costs, and patient health outcomes, such as survival and/or quality of life. The multitude of effects of a CDx can be complex, difficult to identify and measure, so at the most basic level there are three essential characteristics that need to be considered when conducting an economic evaluation of a CDx [42]. First, it is important to have an accurate estimate of the prevalence of the condition, biomarker, or omics signature that will be identified using the CDx. This information is essential as it can begin to shed light on whether or not testing with the CDx will be worthwhile. For example, a CDx that identifies patients who may benefit from a targeted therapy would be of little use if the prevalence of the biomarker of interest were very high, as it is likely that similar outcomes could be achieved by simply treating all patients. The same CDx would also be of little use if the prevalence of the biomarker of interest were very low, as not testing anyone and withholding treatment for all patients could achieve similar outcomes. Second, the analytical validity of the CDx (i.e., its sensitivity and specificity or how well the CDx predicts the presence or absence of a particular biomarker of interest) [43] plays an important role in the economic evaluation of a CDx. Even a small amount of imperfection in analytical validity can undermine the cost-effectiveness of a CDx and associated targeted therapy [33]. For example, if a CDx has a high false-positive rate, more patients will receive an inappropriate treatment, resulting in an increase in treatment costs for no additional increase in health outcomes as well as the potential harms from toxicity [44]. False-negative results can also lead to delayed treatment, which can potentially have negative impacts on health outcomes. Third, the health outcomes of the available treatment (or withholding treatment) that is directed by a CDx affect the value of the testing approach. That is, does the CDx have clinical utility by providing information about diagnosis, treatment management, or prevention of a disease that impacts clinical decision-making and outcomes [43]. Without evidence of positive impacts on health outcomes (or in the case of withholding treatment similar outcomes), it is unlikely that a CDx would ever be seen as cost-effective. Overall, these three characteristics of CDx (i.e., prevalence, analytical validity, and treatment outcomes) are essential when considering the cost-effectiveness of a CDx and should be considered the minimum criteria that are incorporated into an economic evaluation. Ideally, other criteria should also be considered in the economic evaluation of CDx, beyond the three essential characteristics. The optimal patient population to receive testing and the most appropriate positioning of testing in the patient care pathway (e.g., before or after first-line therapy) should be considered. That is, not only should a consideration be made as to what patients should receive testing but also when patients should be tested. Any economic evaluation of a CDx should also ensure that all relevant comparators are being assessed. For example, if there are multiple CDx available to be used in clinical

COMPANION AND COMPLEMENTARY DIAGNOSTICS

20.3 ECONOMIC EVALUATION OF COMPANION DIAGNOSTICS

387

practice, their relative impacts on costs and health outcomes should all be determined (e.g., CDx developed by different manufacturers and regulatory approved and nonregulatory approved laboratory developed diagnostics). Inclusion of all relevant comparators is not solely limited to assessing different types of the same CDx, but consideration of nontesting strategies, the use of multiple tests or sequences of tests, alternative classification options, and alternative treatment decisions from test results should also be made [42]. An exemplar of a comprehensive appraisal of all possible testing, classification, and treatment options is an economic evaluation of the 21-gene assay for guiding adjuvant chemotherapy decisions in early breast cancer [45,46]. This decision analytic model evaluated a number of possible testing and treatment options, including three unique outcomes for the initial test, four unique outcomes for the second test, and two unique outcomes from the treatment decision. This resulted in 12 unique risk groups, 24 unique patient pathways, and 1000 unique strategies to compare in the analysis [46]. However, it is important to note that such comprehensive analyses can come with the drawback of indicating a very prescriptive “optimal” or most cost-effective strategy that does not consider heterogeneity in patient and clinician treatment preferences. For example, although it is most cost-effective for all high-risk women to receive chemotherapy, some women will not want to undergo treatment and similarly although it is not cost-effective for all low-risk women to receive chemotherapy, some may want to undergo treatment anyways. It may therefore be important to account for this heterogeneity in treatment preferences in an economic evaluation by allowing for the possibility that some women in each risk category will receive chemotherapy, while other women of a similar risk do not receive chemotherapy [45]. Finally, consideration should also be given to when an economic evaluation of a CDx should be conducted. Early evaluation of tests can facilitate efficient development, where insights and evidence should be continuously integrated into its evaluation [47]. In fact, decision analytic modeling allows for an iterative approach to the evaluation of a CDx, in that, uncertainty in parameters that can affect the cost-effectiveness of a CDx can be identified early in the development process and research can be specifically designed to reduce this uncertainty and the model eventually updated as additional evidence is generated [13].

20.3.2 Approaches Used in the Economic Evaluation of Companion Diagnostics There are two main approaches used in the economic evaluation of CDx, the selection of which is mainly dependent on the level of evidence available to support the clinical utility of the CDx [36,48]. These approaches include (1) the comparative effectiveness approach (i.e., there is direct evidence of the CDx’s impact on health outcomes, such as overall survival and/or quality of life) and (2) the linked evidence approach (i.e., there is only indirect evidence of the CDx’s impact on health outcomes). 20.3.2.1 Comparative Effectiveness Approach The comparative effectiveness approach can only be used when there is direct evidence of the impact of testing on health outcomes, ideally derived from the results of a

COMPANION AND COMPLEMENTARY DIAGNOSTICS

388

20. ECONOMIC EVALUATION OF COMPANION AND COMPLEMENTARY DIAGNOSTICS

randomized controlled trial. Generally, such direct evidence is not available due to the dearth of clinical trials assessing medical tests [22], but increasingly, trials are being conducted that can support the evaluation of CDx in this manner. For example, the TAILORx trial has shown that women identified to be at intermediate risk of breast cancer recurrence using a 21-gene expression test can avoid the use of chemoendocrine therapy as disease-free survival when only taking endocrine therapy was similar [25]. Another trial in Crohn’s disease, named PROFILE, that is currently ongoing, is aiming to develop evidence on sustained surgery and steroid-free remission rates associated with a prognostic test that identifies patients that require infliximab treatment immediately (top-down approach) or if treatment with infliximab can be reserved only if the disease flares recurrently (step-up approach) [49]. It should be noted that the likelihood of direct evidence being available is higher for CDx compared to other medical tests, due to the fact that CDx are sometimes directly associated and codeveloped with a treatment. There are, however, a number of examples of CDx, including the two discussed earlier that were not developed concurrently with a treatment (e.g., EGFR mutation testing for directing the use of erlotinib or gefitinib). Another benefit of conducting a trial for a CDx is that the comparative effectiveness approach can be facilitated through embedding a trial-based economic evaluation into the design of the study, as has been done for the PROFILE trial. This way, an accurate assessment of the cost-effectiveness of the CDx over the trial follow-up period can be conducted and decision makers can be more confident in their adoption/funding decision. However, these trials can take time to set up, collect, and analyze the data, which can delay uptake of the CDx in clinical practice. Test developers may therefore seek alternative ways to develop economic evidence to support early reimbursement of their CDx while the trial evidence is being collected. 20.3.2.2 Linked Evidence Approach As mentioned previously, there is a dearth of direct evidence of the impact of CDx on health outcomes (i.e., clinical utility), as this information can be difficult, costly, or unethical to obtain through the conduct of a randomized controlled trial [22]. To build an economic argument that incorporates the impact of the CDx on health outcomes and maximize the available information for decision makers, it may be necessary to rely on a synthesis of existing evidence using a decision analytic modeling framework. However, if such an approach is to be taken, it is necessary that the framework is systematic in the collection and critique of the available evidence to avoid unrealistic and biased cost-effectiveness results. The linked evidence approach is one of such frameworks, where disparate sources of evidence associated with different components of the test treat pathway are used to predict the impact of testing on health outcomes (i.e., evidence that links analytical validity to clinical decision-making and treatment effectiveness, including the impacts on patients that receive false-positive and false-negative results) [35,44,50]. This information can then support parameterization of a decision analytic model that can be used to estimate the cost-effectiveness of alternative testing strategies. When applying the linked evidence approach, it is important to consider that test characteristics, such as analytical validity, can vary depending on the population tested, type

COMPANION AND COMPLEMENTARY DIAGNOSTICS

20.3 ECONOMIC EVALUATION OF COMPANION DIAGNOSTICS

389

of test used, and definition of test positivity. Furthermore, CDx developed to better stratify existing treatments can alter the spectrum of disease severity relative to the patient populations in which evidence associated with clinical decision-making and treatment effectiveness was collected. Therefore, existing evidence sources may not always be appropriate for linking if the patient populations are not sufficiently comparable. Furthermore, with each linkage made in the synthesis, the uncertainty regarding the transferability between linkages increases [35]. Details of the data supporting the linkages therefore need to be rigorously reviewed, critiqued, and specified explicitly, so decision makers can make judgments about the appropriateness of the approach and any resulting uncertainty in the cost-effectiveness estimates. In some cases, the CDx may not alter the spectrum of disease compared to the patient population in which the evidence of treatment effectiveness was collected or effectiveness of the indicated treatment might be well established across the disease spectrum. An abridged version of the linked evidence approach in such cases may be appropriate, where the linkage of evidence can be limited to the impact of the CDx’s analytical validity on clinical decision-making [35]. There are a number of challenges in using the linked evidence approach. The most obvious being the potential difficulty in finding appropriate evidence to support all areas of the test treat pathway that require linkage. This is particularly a problem when trying to identify evidence for patients who have been ruled out from receiving a targeted treatment and it is relatively unknown what treatment they will eventually receive and if they will receive any additional testing. Similar issues also exist for identifying health outcomes for patients who have false-negative or false-positive test results. A linked evidence approach can also be problematic when the evidence of clinical utility is limited to surrogate outcomes (e.g., response rates, reduction in tumor size). This is a common occurrence in the evaluation of oncology drugs [51] and ultimately requires that additional evidence be generated either in the form of a trial or from observational data indicating an association between surrogate and patient-relevant outcomes, such as progression-free or overall survival. Finally, innovative CDx might not have an appropriate reference standard to which analytical validity can be reliably assessed, which may necessitate assumptions of perfect accuracy. However, to address all the challenges listed earlier, analysts will need to be very transparent in any assumptions that are made and that appropriate sensitivity analyses are conducted so decision makers can understand the impacts of the assumptions and resulting uncertainty on the cost-effectiveness results. Despite its limitations, the linked evidence approach is considered acceptable by a number of health technology assessment agencies worldwide [52]. It is likely that decision makers value such an approach to evaluation as it can help reduce the uncertainty associated with making an adoption/funding decision. This is evident in the fact that the application of a linked evidence approach has been shown to be associated with a reduced probability of an interim funding decision requiring the CDx manufacturer to collect additional evidence [50]. However, the approach should not be considered a replacement for direct evidence of clinical utility and is only recommended when direct evidence cannot be collected or such evidence is concurrently being collected in an ongoing trial.

COMPANION AND COMPLEMENTARY DIAGNOSTICS

390

20. ECONOMIC EVALUATION OF COMPANION AND COMPLEMENTARY DIAGNOSTICS

20.3.3 Economic Evaluation of Multiple Companion Diagnostics Used in Combination Increasingly, many treatment pathways rely on not only the results from a single CDx but also the results from multiple CDx, either preformed in parallel or in sequence (e.g., immunohistochemistry testing followed by fluorescence in situ hybridization testing to identify HER2 overexpression in breast cancer). However, limited evidence exists as to how different testing strategies (e.g., assumptions of parallel or sequential testing or what criteria should be used to indicate that a second test should be used) should be incorporated into an economic evaluation and what impact different modeling approaches can have on the results of an analysis [33]. There are a number of reasons for the lack of consideration, including the fact that when employing standard decision analytic modeling methods, such as decision trees or Markov models [53], evaluation of multiple CDx with all possible options considered can become increasingly complex and difficult to conduct [54,55]. Furthermore, it is also likely that the use of one CDx will impact the use of a second CDx, in that they are not independent of each other (e.g., the first test will impact the analytical validity of the second test, in that, the case mix of patients is likely to change based on the use of the first test). However, evidence of performance dependency is usually lacking for CDx used together and therefore the majority of existing economic evaluations treat multiple CDx as independent, which may lead to biased estimates of cost-effectiveness [56]. For example, the probability of an event/condition given the result of two CDx can be calculated by multiplying the odds of the event/condition after the first test by the likelihood ratio from the second test. However, when the two tests are correlated, the disease probability is overestimated, highlighting the importance of considering correlation between serial tests when calculating posttest probability [57]. In the context of economic evaluation, Novielli et al. have shown that accounting for the performance dependency of two diagnostic tests for deep vein thrombosis (i.e., performance of the second test may differ depending on the results of the first test) can result in different conclusions about cost-effectiveness compared with models that assume test independence [58]. Building on this, Longo et al. [59] noted that when a test is used for monitoring, the results of the first administration of the test and responses to it change the case mix of patients (i.e., prevalence of the biomarker) at the second administration of the test. This may affect the test performance and hence the value of the test, making these issues of performance dependency important to consider. To address this issue, researchers have suggested that sensitivity analyses be conducted that assess the impact of different within-individual correlation between the tests or that primary studies be conducted that consider the entire test and treat pathway as the intervention rather than only independent single components [56]. However, the latter approach is likely to be difficult given numerous test and treat strategies that could be possible. Furthermore, as testing with CDx becomes more complex, involving multiple sequential or parallel tests, with multiple potential courses of action, standard decision analytic modeling using Markov processes may become inefficient. Analysts will therefore likely need to explore the use of optimization methods that can chose the most cost-effective testing options from hundreds or thousands of potential testing strategies. However,

COMPANION AND COMPLEMENTARY DIAGNOSTICS

20.3 ECONOMIC EVALUATION OF COMPANION DIAGNOSTICS

391

published examples of such approaches to the economic evaluation of CDx are very limited [60] and are an area requiring further research.

20.3.4 Alternative Approaches to the Economic Evaluation of Companion Diagnostics As the complexity in developing and applying CDx in clinical practice increases, there may be a need to apply alternative approaches in their economic evaluation. To increase the likelihood of developing a cost-effective CDx, approaches designed to identify the optimal operating point (i.e., the sensitivity and specificity pair at which a CDx is most costeffective) may be of benefit to developers of CDx. CDx that results in complex treatment decisions, involving multiple possible options, will also necessitate alternative approaches to ensure that the cost-effectiveness of the CDx can be accurately estimated. 20.3.4.1 Approaches Based on Analytical Validity In contrast to information on clinical utility, data on analytical validity (i.e., sensitivity and specificity) is usually available for a CDx. Most CDx produce a numerical result that needs to be categorized into what is considered a positive and negative result using some cutoff (threshold) value. As the threshold is lowered, sensitivity will increase and specificity will decrease, necessitating the selection of a threshold that appropriately balances these two elements [61]. However, thresholds are often arbitrary or chosen based on clinical suitability, rather than selected based on weighing up the consequences of different rates of false-negative and false-positive results on health outcomes and costs. Ideally, sensitivity and specificity values for multiple different threshold values should be plotted using a summary receiver operating characteristic (ROC) curve to identify the optimal operating point (i.e., the sensitivity and specificity pair at which a CDx is most cost-effective) [62]. The identification of the optimal operating point is important, as when conducting economic evaluations of CDx, analysts will need to synthesize and incorporate evidence of the CDx’s analytical validity into a decision analytic model. There may, however, be multiple studies reporting different sensitivity and specificity values, potentially using different thresholds, leading to difficulties in choosing the most appropriate values to include in the evaluation. In practice, very few evaluations of CDx account for the potential impact of different thresholds on analytical validity and subsequent estimates of cost-effectiveness [33]. Furthermore, it is even more unlikely for evaluations to make recommendations about what thresholds should be used in routine clinical practice [56]. Instead most evaluations simply conduct univariate sensitivity analyses on sensitivity and specificity parameters, which ignores their correlation [33]. It is therefore recommended that sensitivity and specificity values be reported across all potential thresholds so that they can be incorporated into economic evaluations and the threshold at which the CDx is most costeffective is identified. Identification of the optimal operating point is not only essential for an accurate estimation of the cost-effectiveness of a CDx but can also be used to facilitate efficient development of a new CDx. To maximize the possibility of a CDx being cost-effective, it is

COMPANION AND COMPLEMENTARY DIAGNOSTICS

392

20. ECONOMIC EVALUATION OF COMPANION AND COMPLEMENTARY DIAGNOSTICS

important that evidence to support the clinical utility of a CDx has been collected in a patient population that has been identified using the most appropriate threshold value (i.e., the optimal operating point). If an alternative threshold is used, developers of CDx risk the associated evidence of clinical utility being collected in a suboptimal patient population, potentially having a negative impact on the cost-effectiveness of the CDx. There are a number of approaches by which analysts can identify the optimal operating point for a CDx [61,63,64]. All the approaches rely on the principle that an ROC curve can be generated and the sensitivity and specificity pair that results in the test having the largest net monetary benefit is selected as the optimal operating point. Briefly, net monetary benefit can be considered equivalent to the ICER approach to economic evaluation [65] and is calculated as the product of individual-level health outcomes (e.g., QALYs) and the upper limit a decision maker is willing to pay for an additional unit of outcome minus the costs associated with an intervention [66]. That is, larger, positive values of net monetary benefit are associated with more cost-effective interventions, in that the value of the outcomes produced is greater than the associated costs [66]. The approaches for identifying the optimal operating point have, however, been minimally used in the practical evaluation of CDx and other medical tests, largely due to their complexity. More straightforward approaches have also been proposed, but again their practical application has been limited as they rely on the availability of multiple studies reporting sensitivity and specificity values for different thresholds, which is often not available [62]. Interested readers are referred to a review by Sanghera et al. [62] that summarizes more specific details and methodological differences between the available approaches to identifying a CDx’s optimal operating point. Finally, it should be noted that not only will the choice of threshold affect the sensitivity and specificity pair that should be used in an economic evaluation but also a number of factors that can occur during the testing pathway related to the measurement of analytical validity. In that, the analytical validity of a CDx is associated with uncertainty due to within-individual biological variation (e.g., differences over time), preanalytical variation from the patient (e.g., time and site of sampling, patient state, and preparation), preanalytic variation from technical aspects of testing (e.g., differences in storage, processing, collection, handling, and transport), analytical imprecision (e.g., differences in time, calibration, operators, equipment, and environment), and analytical trueness (e.g., interference and cross-reactivity) [67], which can all have an impact on the cost-effectiveness of a CDx. A greater appreciation of these factors may therefore be important when conducting economic evaluations of CDx and should be considered in future analyses. 20.3.4.2 Approaches for Multiplex Companion Diagnostics Increasingly, CDx are being developed that not only assess the molecular status of a patient for a single biomarker but also assess the presence of multiple biomarkers simultaneously. Results of these multiplex CDx may therefore be used to guide treatment decisions for an array of different interventions, each associated with different costs and outcomes. Application of a multiplex CDx may recommend the use of a particular intervention in a specific patient, but this does not mean that it will be cost-effective for that patient to receive the intervention. That is, the results of a multiplex CDx may be considered clinically actionable for a patient but from an economic standpoint may not always

COMPANION AND COMPLEMENTARY DIAGNOSTICS

20.3 ECONOMIC EVALUATION OF COMPANION DIAGNOSTICS

393

lead to the use of an intervention that is an efficient use of resources and would therefore not be considered “economically actionable.” This is problematic when conducting evaluations as the use of these cost-ineffective treatments may actually mask the cost-effectiveness of the CDx, when in fact, it is the use of certain treatments that are responsible for the poor average cost-effectiveness of the multiplex CDx [68]. Methods to tease out which test results are economically actionable are poorly characterized, but it has been suggested that modeling approaches, such as backward induction [69], could be used to address this issue [68]. Such methods would require multiple iterations of a decision analytic model to be run, where all economically actionable results are first identified and then a final iteration of the model is run where only patients with economically actionable results receive targeted treatment and the remaining patients receive the next best alternative, nontargeted treatment. Such an approach would potentially lead to a more accurate economic assessment of multiplex CDx as only cost-effective treatments would be recommended after testing. Further research is, however, required to determine if this approach is feasible and appropriate for informing adoption/funding decisions for multiplex CDx.

20.3.5 Expanding the Value Framework for Companion Diagnostics As discussed in Sections 2.1.2 and 2.2, the use of ICERs, in the form of cost per QALY gained, is the standard framework in which the results of economic evaluations are used to inform adoption/funding decisions of health-care interventions. However, given that QALYs only consider the health-related impacts of interventions, it has been suggested that such a framework does not adequately capture important nonhealth outcomes related to CDx (e.g., the benefits of the test information itself) [70 74]. CDx can increase certainty that a patient will not experience a serious adverse event or that the patient will be more likely to respond to a particular treatment. This can provide peace of mind, reassurance and potentially increase choice, all of which influence patients’ mental well-being and welfare over and above the expected health outcomes that may be achieved from any treatment received after testing [70]. Even in cases where a CDx identifies patients who are not eligible to receive targeted therapy (e.g., multiplex testing identifies that no targeted treatment is available for a patient’s last line cancer treatment), the information derived from testing is still valued by patients [75]. This is largely because the testing information will enable them to make decisions about how to spend the remainder of their lives. The nonhealth outcomes associated with test information are commonly referred to as the “value of knowing” [70,76], or more specifically “planning value” (i.e., the effect on patients’ ability to make better life decisions, such as choices about reproduction, work, retirement, long-term health, and finances) and “psychic value” (how test information affects patients’ sense of self) [72]. Relatively little research has been conducted to determine what factors influence the value of these nonhealth outcomes and how the value may differ between patients and types of CDx. Initial work suggests that the value associated with these nonhealth outcomes is a function of the accuracy of the test, pretest risk of the event (e.g., risk of drug adverse events based on age, sex, and concomitant medication), the patient’s discount rate (i.e., how much they prefer to receive benefits now compared

COMPANION AND COMPLEMENTARY DIAGNOSTICS

394

20. ECONOMIC EVALUATION OF COMPANION AND COMPLEMENTARY DIAGNOSTICS

to in the future), time to onset of the event (e.g., will the adverse event occur immediately or only after prolonged use of treatment), the degree of a patient’s worry about event onset, and the patient’s aversion to receiving bad news (loss aversion) [72]. There is, however, likely to be heterogeneity, particularly in the “psychic value” associated with a CDx, as each patient will differ in their acceptance of loss aversion and the degree to which they worry about the onset of the event. Generally, it is likely that patients will place a higher value on CDx that rules out the presence of serious events having a low probability of occurrence [72], but of course some patients will rather remain ignorant and not be tested to avoid being aware of the unpleasantness of experiencing the event [77]. The importance of considering these additional nonhealth outcomes will also vary depending on the stakeholder. Payers of health-care interventions (e.g., governments or insurance companies) and clinicians are likely to favor health outcomes such as the impact of the CDx on treatment decisions and the QALYs generated over nonhealth outcomes as these stakeholders will receive (or observe in the case of the treating clinician) little to no benefit from changes in nonhealth outcomes, whereas patients will not only value health outcomes but also test information that can improve their overall welfare. What is not clear, however, is if patients are willing to trade-off health outcomes in order to obtain these additional nonhealth outcomes. This is important, as ultimately allocating finite resources to adopt/fund CDx because they are associated with additional nonhealth outcomes will mean that there will be fewer resources available to facilitate adoption/funding of other interventions that potentially offer greater health gains but are associated with minimal nonhealth outcomes. It will therefore be necessary to determine if and how much health patients, the general public, and decision makers are willing to forgo to obtain nonhealth outcomes associated with test information if such elements of value are to be considered in adoption/funding decisions of CDx. Furthermore, recommending an expanded definition of value solely for the evaluation of CDx is problematic, as special credence would be given to their adoption/funding, despite a number of other health-care interventions also being associated with important nonhealth outcomes [78]. This has led to calls for an expanded value framework that considers more than just health outcomes when evaluating all types of health-care interventions [76]. Additional elements of value can be broadly categorized as the value of knowing and other informational externalities but specifically include consideration of reductions in uncertainty, greater piece of mind against catastrophic health/financial loss (known as insurance value), the value of hope associated with end of life treatment decisions, real option value (e.g., accessing treatment today means access to future innovations may be possible), and externalities arising from scientific advances (e.g., the development of pertuzumab after trastuzumab). However, there is very little research on how the elements of an expanded value framework would be measured, integrated, and weighted to inform adoption/funding decisions. This is particularly problematic as the trade-off between different test attributes, especially when multiple interactions and trade-offs need to be considered, is currently poorly understood [79]. Questions as to what quantitative approaches could be used to incorporate weights that different patient groups might place on different mixes of test characteristics and associated health and nonhealth outcomes need to be explored to

COMPANION AND COMPLEMENTARY DIAGNOSTICS

REFERENCES

395

further understand the implications of allocating health-care resources within such a framework.

20.4 SUMMARY Overall, the application of economic evaluation to inform decision-making regarding the adoption/funding of CDx provides a number of unique challenges for health economists. The main factors affecting the value of a CDx will be the prevalence of the biomarker of interest, the analytical validity of the CDx, and the health outcomes associated with the treatment, which all are essential factors to consider when evaluating a CDx. In many cases, economic evaluation of CDx will also need to consider the availability of multiple tests, the positioning of testing within the care pathway, assessment of all possible test and treatment strategies, and the impact of patient/clinician choice on the selection of the optimal test and treat strategy. The available evidence with regards to the CDx’s clinical utility will also impact on the approach to evaluation that can be conducted. The comparative effectiveness approach although ideal may not be possible and the linked evidence approach is commonly accepted and has been shown to facilitate decision-making under uncertainty in the adoption/funding of CDx. Given that testing strategies are becoming more complex and potentially multiple CDx will be used to guide patient management, it will also be essential to consider the dependency in their performance to accurately estimate the cost-effectiveness of different test and treat strategies. Furthermore, test development should be guided by the identification of a CDx’s optimal operating point and efforts should be made to identify the most efficient treatment options associated with CDx that indicate use of multiple treatments to ensure an accurate assessment of cost-effectiveness. Finally, the nonhealth outcomes associated with CDx may be valuable in supporting adoption/funding decisions, but patients, the public, and decision makers should be aware that their consideration would necessitate a trade-off between health and nonhealth outcomes. Thus requires a standard framework that can be applied to the evaluation of all types of health-care interventions, if the impact of nonhealth outcomes is to be considered in adoption/funding decisions. Overall, this chapter has highlighted that there is a need for further refinement and standardization of economic evaluation methods to ensure consistent decisions regarding the adoption/funding of CDx can be made and that patients receive CDx testing that results in improved outcomes at an acceptable cost to payers and society.

References [1] Drummond MF, Sculpher MJ, Torrance GW, et al. Methods for the economic evaluation of health care programmes. 3rd ed New York: Oxford University Press Inc; 2005. [2] Gray AM, Clarke PM, Wolstenholme JL, et al. Applied methods of cost-effectiveness analysis in health care. Oxford: Oxford University Press Inc; 2011. [3] Torrance GW, Thomas WH, Sackett DL. A utility maximization model for evaluation of health care programs. Health Serv Res 1972;7(2):118 33.

COMPANION AND COMPLEMENTARY DIAGNOSTICS

396

20. ECONOMIC EVALUATION OF COMPANION AND COMPLEMENTARY DIAGNOSTICS

[4] Torrance GW. Social preferences for health states—empirical evaluation of 3 measurement techniques. Socio Econ Plan Sci 1976;10(3):129 36. [5] Brooks R. EuroQol: the current state of play. Health Policy 1996;37(1):53 72. [6] Rowen D, Brazier J, Young T, et al. Deriving a preference-based measure for cancer using the EORTC QLQC30. Value Health 2011;14(5):721 31. [7] Xie F, Gaebel K, Perampaladas K, et al. Comparing EQ-5D valuation studies: a systematic review and methodological reporting checklist. Med Decis Making 2014;34(1):8 20. [8] Briggs AH, O’Brien BJ. The death of cost-minimization analysis? Health Econ 2001;10(2):179 84. [9] Dakin H, Wordsworth S. Cost-minimisation analysis versus cost-effectiveness analysis, revisited. Health Econ 2013;22(1):22 34. [10] Klose T. The contingent valuation method in health care. Health Policy 1999;47(2):97 123. [11] Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ 2000;320(7248):1530 3. [12] Johannesson M, Jonsson B. Economic evaluation in health care: is there a role for cost-benefit analysis? Health Policy 1991;17(1):1 23. [13] Briggs A, Claxton K, Sculpher M. Decision modelling for health economic evaluation. New York: Oxford University Press Inc; 2006. [14] Glick HA, Doshi JA, Sonnad SS, et al. Economic evaluation in clinical trials. New York: Oxford University Press Inc; 2014. [15] Petrou S, Gray A. Economic evaluation using decision analytical modelling: design, conduct, analysis, and reporting. BMJ 2011;342:d1766. [16] Caro JJ, Briggs AH, Siebert U, et al. Modeling good research practices—overview: a report of the ISPORSMDM modeling good research practices task force 1. Med Decis Making 2012;32(5):667 77. [17] Roberts M, Russell LB, Paltiel AD, et al. Conceptualizing a model: a report of the ISPOR-SMDM modeling good research practices task force—2. Med Decis Making 2012;32(5):678 89. [18] Siebert U, Alagoz O, Bayoumi AM, et al. State-transition modeling: a report of the ISPOR-SMDM modeling good research practices task force—3. Med Decis Making 2012;32(5):690 700. [19] Briggs AH, Weinstein MC, Fenwick EAL, et al. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group 6. Med Decis Making 2012;32(5):722 32. [20] Eddy DM, Hollingworth W, Caro JJ, et al. Model transparency and validation: a report of the ISPOR-SMDM modeling good research practices task force 7. Med Decis Making 2012;32 (5):733 43. [21] Merlin T, Farah C, Schubert C, et al. Assessing personalized medicines in Australia: a national framework for reviewing codependent technologies. Med Decis Making 2013;33(3):333 42. [22] Ferrante di Ruffano L, Davenport C, Eisinga A, et al. A capture recapture analysis demonstrated that randomized controlled trials evaluating the impact of diagnostic tests on patient outcomes are rare. J Clin Epidemiol 2012;65(3):282 7. [23] Thompson AJ, Newman WG, Elliott RA, et al. The cost-effectiveness of a pharmacogenetic test: a trial-based evaluation of TPMT genotyping for azathioprine. Value Health 2014;17(1):22 33. [24] Karapetis CS, Khambata-Ford S, Jonker DJ, et al. K-ras mutations and benefit from cetuximab in advanced colorectal cancer. N Engl J Med 2008;359(17):1757 65. [25] Sparano JA, Gray RJ, Makower DF, et al. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N Engl J Med 2018;379(2):111 21. [26] Kwak EL, Bang YJ, Camidge DR, et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N Engl J Med 2010;363(18):1693 703. [27] Flaherty KT, Puzanov I, Kim KB, et al. Inhibition of mutated, activated BRAF in metastatic melanoma. N Engl J Med 2010;363(9):809 19. [28] Jakka S, Rossbach M. An economic perspective on personalized medicine. HUGO J 2013;7(1):1. [29] Mathur S, Sutton J. Personalized medicine could transform healthcare. Biomed Rep 2017;7(1):3 5. [30] Blair ED. Molecular diagnostics and personalized medicine: value-assessed opportunities for multiple stakeholders. Pers Med 2010;7(2):143 61. [31] Zaric GS. Cost implications of value-based pricing for companion diagnostic tests in precision medicine. PharmacoEconomics 2016;34(7):635 44.

COMPANION AND COMPLEMENTARY DIAGNOSTICS

REFERENCES

397

[32] Trikalinos TA, Siebert U, Lau J. Decision-analytic modeling to evaluate benefits and harms of medical tests: uses and limitations. Med Decis Making 2009;29(5):E22 9. [33] Doble B, Tan M, Harris A, et al. Modeling companion diagnostics in economic evaluations of targeted oncology therapies: systematic review and methodological checklist. Expert Rev Mol Diagn 2015;15(2):235 54. [34] Garrison LP, Austin MJF. The economics of personalized medicine: a model of incentives for value creation and capture. Drug Inf J 2007;41(4):501 9. [35] Merlin T, Lehman S, Hiller JE, et al. The “linked evidence approach” to assess medical tests: a critical analysis. Int J Technol Assess Health Care 2013;29(3):343 50. [36] Garfield S, Polisena J, Ss D, et al. Health technology assessment for molecular diagnostics: practices, challenges, and recommendations from the Medical Devices and Diagnostics Special Interest Group. Value Health 2016;19(5):577 87. [37] Faulkner E, Annemans L, Garrison L, et al. Challenges in the development and reimbursement of personalized medicine-payer and manufacturer perspectives and implications for health economics and outcomes research: a report of the ISPOR personalized medicine special interest group. Value Health 2012;15 (8):1162 71. [38] National Institute for Health and Clinical Excellence (NICE). Diagnostics assessment programme manual. Manchester: NICE; 2011. [39] Medical Services Advisory Committe (MSAC). Technical guidelines for preparing assessment reports for the Medical Services Advisory Committee—service type: investigative (version 3.0). Canberra: Australian Government Department of Health; 2017. [40] Medical Services Advisory Committe (MSAC). Technical guidelines for preparing assessment reports for the Medical Services Advisory Committee—medical service type: therapeutic (version 2.0). Canberra: Australian Government Department of Health; 2016. [41] Candian Agency for Drugs and Technologies in Health (CADTH). Health technology assessment of drugs with companion diagnostics at CADTH. Ottawa, ON: CADTH; 2017. [42] Soares MO, Walker S, Palmer SJ, et al. Establishing the value of diagnostic and prognostic tests in health technology assessment. Med Decis Making 2018;38(4):495 508 272989X17749829. [43] Bossuyt PM, Reitsma JB, Linnet K, et al. Beyond diagnostic accuracy: the clinical utility of diagnostic tests. Clin Chem 2012;58(12):1636 43. [44] Merlin T. The use of the ‘linked evidence approach’ to guide policy on the reimbursement of personalized medicines. Pers Med 2014;11(4):435 48. [45] Paulden M, Franek J, Pham B, et al. Cost-effectiveness of the 21-gene assay for guiding adjuvant chemotherapy decisions in early breast cancer. Value Health 2013;16(5):729 39. [46] Health Quality Ontario. Gene expression profiling for guiding adjuvant chemotherapy decisions in women with early breast cancer: an evidence-based and economic analysis. Ont Health Technol Assess Ser 2010;10 (23):1 57. [47] Buisman LR, Rutten-van Molken M, Postmus D, et al. The early bird catches the worm: early costeffectiveness analysis of new medical tests. Int J Technol Assess Health Care 2016;32(1/2):46 53. [48] Byron SK, Crabb N, George E, et al. The health technology assessment of companion diagnostics: experience of NICE. Clin Cancer Res 2014;20(6):1469 76. [49] Lee J, Biasci D, Noor N, et al. OC-044 Profile trial: predicting outcomes for crohn’s disease using a molecular biomarker. Gut 2017;66(Suppl 2):A22 3. [50] Merlin TL, Hiller JE, Ryan P. Impact of the “linked evidence approach” method on policies to publicly fund diagnostic, staging, and screening medical tests. MDM Policy Pract 2016;1(1) 2381468316672465. [51] Kemp R, Prasad V. Surrogate endpoints in oncology: when are they acceptable for regulatory and clinical decisions, and are they currently overused? BMC Med 2017;15(1):134. [52] Staub LP, Dyer S, Lord SJ, et al. Linking the evidence: intermediate outcomes in medical test assessments. Int J Technol Assess Health Care 2012;28(1):52 8. [53] Sonnenberg FA, Beck JR. Markov models in medical decision making: a practical guide. Med Decis Making 1993;13(4):322 38. [54] Doble B, Harris A, Thomas DM, et al. Multiomics medicine in oncology: assessing effectiveness, costeffectiveness and future research priorities for the molecularly unique individual. Pharmacogenomics 2013;14(12):1405 17.

COMPANION AND COMPLEMENTARY DIAGNOSTICS

398

20. ECONOMIC EVALUATION OF COMPANION AND COMPLEMENTARY DIAGNOSTICS

[55] Love-Koh J, Peel A, Rejon-Parilla JC, et al. The future of precision medicine: potential impacts for health technology assessment. PharmacoEconomics 2018;36(12):1439 51. [56] Shinkins B, Yang Y, Abel L, et al. Evidence synthesis to inform model-based cost-effectiveness evaluations of diagnostic tests: a methodological review of health technology assessments. BMC Med Res Methodol 2017;17 (1):56. [57] van Walraven C, Austin PC, Jennings A, et al. Correlation between serial tests made disease probability estimates erroneous. J Clin Epidemiol 2009;62(12):1301 5. [58] Novielli N, Cooper NJ, Sutton AJ. Evaluating the cost-effectiveness of diagnostic tests in combination: is it important to allow for performance dependency? Value Health 2013;16(4):536 41. [59] Longo R, Baxter P, Hall P, et al. Methods for identifying the cost-effective case definition cut-off for sequential monitoring tests: an extension of Phelps and Mushlin. PharmacoEconomics 2014;32(4):327 34. [60] Wright SJ, Gray E, Donten A, Payne K. Constraints, Capacity and Capability: An Application of Mathematical Optimisation Methods. In: Medical Decision Making. 7th ed. Vol. 36. 2016. Available from, http://dx.doi.org/10.1177/0272989X16665121. [61] Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 1993;39(4):561 77. [62] Sanghera S, Orlando R, Roberts T. Economic evaluations and diagnostic testing: an illustrative case study approach. Int J Technol Assess Health Care 2013;29(1):53 60. [63] Phelps CE, Mushlin AI. Focusing technology assessment using medical decision theory. Med Decis Making 1988;8(4):279 89. [64] Laking G, Lord J, Fischer A. The economics of diagnosis. Health Econ 2006;15(10):1109 20. [65] Laska EM, Meisner M, Siegel C, et al. Ratio-based and net benefit-based approaches to health care resource allocation: proofs of optimality and equivalence. Health Econ 1999;8(2):171 4. [66] Stinnett AA, Mullahy J. Net health benefits: a new framework for the analysis of uncertainty in costeffectiveness analysis. Med Decis Making 1998;18(2 Suppl):S68 80. [67] Smith AF, Messenger M, Hall P, et al. The role of measurement uncertainty in health technology assessments (HTAs) of in vitro tests. PharmacoEconomics 2018;36(7):823 35. [68] Doble B, John T, Thomas D, 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 2017;107:22 35. [69] Hay JW. Evaluation and review of pharmacoeconomic models. Expert Opin Pharmacother 2004;5(9):1867 80. [70] Garrison LP, Jr., Mestre-Ferrandiz J, Zamora B. The value of knowing and knowing the value: improving health technology assessment of complementary diagnostics. In: White paper. Office of Health Economics and the European Personalised Medicine Association; 2016. [71] Garau M, Towse A, Garrison L, et al. Can and should value-based pricing be applied to molecular diagnostics? Pers Med 2013;10(1):61 72. [72] Lee DW, Neumann PJ, Rizzo JA. Understanding the medical and nonmedical value of diagnostic testing. Value Health 2010;13(2):310 14. [73] Asch DA, Patton JP, Hershey JC. Knowing for the sake of knowing: the value of prognostic information. Med Decis Making 1990;10(1):47 57. [74] Doble B. Budget impact and cost-effectiveness: can we afford precision medicine in oncology? Scand J Clin Lab Invest Suppl 2016;245:S6 11. [75] Neumann PJ, Cohen JT, Hammitt JK, et al. Willingness-to-pay for predictive tests with no immediate treatment implications: a survey of US residents. Health Econ 2012;21(3):238 51. [76] Garrison Jr. LP, Kamal-Bahl S, Towse A. Toward a broader concept of value: identifying and defining elements for an expanded cost-effectiveness analysis. Value Health 2017;20(2):213 16. [77] Neumann PJ, Hammitt JK, Mueller C, et al. Public attitudes about genetic testing for Alzheimer’s disease. Health Aff 2001;20(5):252 64. [78] Benning TM, Alayli-Goebbels AFG, Aarts M-J, et al. Exploring outcomes to consider in economic evaluations of health promotion programs: what broader non-health outcomes matter most? BMC Health Serv Res 2015;15:266. [79] Thompson M, Weigl B, Fitzpatrick A, et al. More than just accuracy: a novel method to incorporate multiple test attributes in evaluating diagnostic tests including point of care tests. IEEE J Transl Eng Health Med 2016;4:2800208.

COMPANION AND COMPLEMENTARY DIAGNOSTICS