Modeling companion diagnostics in economic evaluations of targeted oncology therapies: systematic review and methodological checklist.

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SPECIAL FOCUS y ‘In vitro companion diagnostics’

Review

Modeling companion diagnostics in economic evaluations of targeted oncology therapies: systematic review and methodological checklist Expert Rev. Mol. Diagn. Early online, 1–20 (2014)

Brett Doble*1, Marcus Tan1,2, Anthony Harris1 and Paula Lorgelly1 1 Centre for Health Economics, Faculty of Business and Economics, Monash University, Room 278, Level 2, Building 75 Monash University, Clayton, Victoria 3800, Australia 2 Deakin Health Economics, Deakin Strategic Research Centre – Population Health, Faculty of Health, Deakin University, 221 Burwood Hwy, Burwood, Victoria 3125, Australia *Author for correspondence: Tel.: +61 399 051 100 Fax: +61 399 058 344 [email protected]

The successful use of a targeted therapy is intrinsically linked to the ability of a companion diagnostic to correctly identify patients most likely to benefit from treatment. The aim of this study was to review the characteristics of companion diagnostics that are of importance for inclusion in an economic evaluation. Approaches for including these characteristics in model-based economic evaluations are compared with the intent to describe best practice methods. Five databases and government agency websites were searched to identify model-based economic evaluations comparing a companion diagnostic and subsequent treatment strategy to another alternative treatment strategy with model parameters for the sensitivity and specificity of the companion diagnostic (primary synthesis). Economic evaluations that limited model parameters for the companion diagnostic to only its cost were also identified (secondary synthesis). Quality was assessed using the Quality of Health Economic Studies instrument. 30 studies were included in the review (primary synthesis n = 12; secondary synthesis n = 18). Incremental cost-effectiveness ratios may be lower when the only parameter for the companion diagnostic included in a model is the cost of testing. Incorporating the test’s accuracy in addition to its cost may be a more appropriate methodological approach. Altering the prevalence of the genetic biomarker, specific population tested, type of test, test accuracy and timing/sequence of multiple tests can all impact overall model results. The impact of altering a test’s threshold for positivity is unknown as it was not addressed in any of the included studies. Additional quality criteria as outlined in our methodological checklist should be considered due to the shortcomings of standard quality assessment tools in differentiating studies that incorporate important test-related characteristics and those that do not. There is a need to refine methods for incorporating the characteristics of companion diagnostics into model-based economic evaluations to ensure consistent and transparent reimbursement decisions are made. KEYWORDS: companion diagnostics • cost-effectiveness analysis • economic evaluation • genomic testing • oncology • precision medicine

Stratifying patients into genetic subgroups for targeted treatment has become increasingly prevalent in oncology [1]. Identification of these subgroups requires the use of diagnostic tests (i.e., companion diagnostics) to detect biomarkers (e.g., protein overexpression or presence of genetic mutations in tumor DNA) predictive informahealthcare.com

10.1586/14737159.2014.929499

of treatment response or lack thereof [2]. These diagnostic tests are intrinsically linked to the efficacious use of targeted therapies and therefore an assessment of economic value should consider their combined impact. Targeted treatment strategies using companion diagnostics are actually a combination of

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Review

Doble, Tan, Harris & Lorgelly

two elements: a single diagnostic strategy, which might involve one or more diagnostic test and alternative treatment strategies for the genetic subgroup under consideration [3]. This complicates the application of traditional methods for economic evaluation, which is usually limited to the assessment of a single intervention in isolation. Model-based economic evaluations are typically used to assess oncology therapies for reimbursement purposes and their methods and use are well established [4,5]. This is in contrast to economic evaluation methods for diagnostic tests, which have not been thoroughly developed and lack consensus on the most appropriate approach [6]. Evaluating a diagnostic test mainly involves determining how well the test can classify patients into appropriate subgroups. In addition, the costs and benefits for all patients who receive the diagnostic test whether or not they eventually receive targeted therapy also need to be considered. An economic evaluation of a companion diagnostic and associated targeted therapy therefore must address whether both the information generated from the diagnostic test and the expected outcomes from the targeted therapy justify their costs. The use and type of diagnostic testing strategy has been shown to be an important influence on the cost-effectiveness of a targeted therapy [7]. This impact is largely due to the analytical validity (i.e., sensitivity and specificity) of diagnostic tests and the resultant mistreatment of false-positive and falsenegative patients. For example, false-positive EGFR non-smallcell lung cancer patients can have worse outcomes with targeted therapy, gefitinib compared with standard chemotherapy carboplatin–paclitaxel and false-negative patients forgo the benefits of targeted treatment [8]. Furthermore, the analytical validity of a test is largely dependent on the arbitrary cut-off (threshold) levels chosen to represent a positive result (i.e., as the test threshold for positivity is lowered, sensitivity will increase and specificity will decrease) [9]. Therefore, it is important to identify the test threshold belonging to the sensitivity and specificity pair at which the test is most cost-effective (i.e., the Optimal Operating Point) [6]. In addition, factors related to the genetic biomarker of interest (e.g., frequency of the mutation) and other platform-specific test characteristics (e.g., the type of test and biochemical methods used to identify the biomarker of interest, the cost of the companion diagnostic and other testing-related costs and the timing/sequence of multiple tests) need to be considered as these are likely to affect the costeffectiveness of the subsequent targeted therapy [3,10,11]. This review systematically assesses the published model-based economic evaluations, in which a targeted oncology therapy has been evaluated alongside a companion diagnostic. Factors specific to the economic evaluation of companion diagnostics are explicitly considered to assess the appropriateness of current research and the impact characteristics related to companion diagnostics might have on the cost-effectiveness of targeted therapies. Approaches for including these characteristics in modelbased economic evaluations are compared with the intent to describe best practice methods for the economic evaluation of companion diagnostics and targeted therapies in oncology. doi: 10.1586/14737159.2014.929499

Methods Overview

A systematic literature review was designed to identify all model-based economic evaluations, in which a companion diagnostic and subsequent targeted therapy was evaluated. The search was limited to the 12 oncology therapies associated with a US FDA-approved companion diagnostic [12]. Overall, the review included a total of 13 therapy/biomarker combinations, given that one therapy (cetuximab) is associated with multiple genetic markers (i.e., EGFR and KRAS). The information extracted from the evaluations was categorized according to methodological characteristics of economic evaluations and genetic information and companion diagnostic test characteristics. Identified economic evaluations were evaluated using the Quality of Health Economic Studies (QHES) instrument [13]. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement [14] was used as a guideline for the design of the review, with adaptations made due to the focus on economic evaluations. Eligibility criteria

Published model-based economic evaluations in the English language comparing a companion diagnostic and subsequent treatment strategy with another alternative treatment strategy for at least one of the 12 therapies of interest were considered. Economic evaluations with an initial trial-based study design with subsequent lifetime model extrapolation were excluded as they were unlikely to consider the impacts of a companion diagnostic. For a study to qualify as having a companion diagnostic and subsequent treatment strategy and therefore be included in the primary synthesis, the economic evaluation had to include, as a minimum, model parameters for the sensitivity and specificity of the companion diagnostic. Studies assessing a targeted therapy in a pre-defined genetic population were excluded as the focus of these types of evaluations were likely to be limited to the targeted therapy without consideration of the impact of the companion diagnostic. Included studies must have also evaluated the test and treatment strategy in the patient population explicitly stated on the respective companion diagnostic’s FDA-approved indications. Economic evaluations of targeted therapies that limited model parameters for the companion diagnostic to only its cost (i.e., do not link the testing strategy through to the impact on treatment decisions and final patient outcomes) and therefore did not include parameters for the sensitivity and specificity of the test in the model (whether this was explicitly mentioned in the study or not) were assessed in a secondary synthesis to provide a critique on this approach to modeling. It is hypothesized that this approach was chosen for simplicity; however, it is thought that any gain in model simplicity as a result of this approach will negatively affect the robustness of the model results. Furthermore, to provide a directly comparable group of studies to those included in the primary synthesis, the secondary synthesis was strictly limited to economic evaluations assessing a targeted therapy that had been included in the primary synthesis. Expert Rev. Mol. Diagn.

Economic evaluations of companion diagnostics

Economic evaluations were limited to cost-effectiveness analyses (measures consequences in natural units, such as life-years [LYs] gained) and cost-utility analyses (consequences are measured in terms of preference-based measures of health, such as quality-adjusted LYs [QALYs]) with an incremental costeffectiveness ratio (ICER) as the study’s main outcome measure.


Information sources & search

We searched five databases: Cochrane via Wiley (technology assessments and economic evaluations), EMB Reviews (Health Technology Assessment and National Health Service Economic Evaluation Database [NHSEED]), EMBASE, Ovid MEDLINE and PubMed from conception to 24 February 2014. Search terms were developed for three different categories: cancer nomenclature, drug names/related information and health economic evaluation terminology. The search terms were initially developed for the Ovid MEDLINE database and modified for each of the respective databases. No restrictions were initially placed on the language of the articles; however, any studies not reported in the English language were excluded from the review during screening. The complete search strategy for all five databases can be found in SUPPLEMENTARY APPENDIX 1 (supplementary material can be found online at www.informahealthcare.com/ suppl/14737159.2014.929499). Health technology assessments and rapid responses from the Canadian Agency for Drugs and Technology in Health [15,16], public summary documents from Australia’s Pharmaceutical Benefits Advisory Committee and Medical Services Advisory Committee [17,18] and technology assessments from the Agency for Healthcare Research and Quality [19] were also searched to locate any additional studies or information relevant for this review. National Institute for Health and Care Excellence single or multiple technology appraisals [20] were not reviewed as it was assumed any report of importance would be published in Health Technology Assessment and therefore captured in the main database search. In addition, a hand search of the quoted references from the included articles was conducted to identify any additional studies. Data collection process

Two authors (BD and MT) independently screened the titles and abstracts of all the citations identified from the search strategies. Full-texts of the citations included after the initial screening were also independently reviewed by those authors. The reviewer agreement was measured using the Cohen’s kappa, which measures the agreement between two raters who each classify N items into C mutually exclusive categories [21]. Discrepancies were resolved by discussions, until a consensus between the authors was reached. One author (BD) extracted the data from all included studies and the data from 25% of the included studies (randomly selected) were also retrieved by another author (MT) to ensure accuracy in the data extraction. Data items

To provide an idea of the overall quality of the included studies, standard methodological elements including: research informahealthcare.com

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question, target decision maker, type of model (decision tree, Markov, etc.), time horizon and discounting applied, characterization of uncertainty and therapies/strategies evaluated were extracted. Also extracted were the reported overall model results and results from sensitivity analyses. ICERs were inflated and converted to 2013 US dollars using the CCEMG – EPPICentre Cost Converter [22]. To determine the impact of various test-related characteristics and different modeling approaches for incorporating companion diagnostics into an economic evaluation of a targeted oncology therapy, included studies were assessed for their consideration of a number of test-related characteristics identified to be of importance in previous reviews [6,10,11]: • Prevalence of the genetic biomarker detected by the companion diagnostic in the population of interest; • Cost of the companion diagnostic and inclusion of other testing-related costs (e.g., cost of sample collection, cost of repeat testing and cost of interpreting test results); • Type/specific manufacturer of the diagnostic test assessed (e.g., was the test included in the model a companion diagnostic or laboratory developed diagnostic); • Test accuracy (i.e., sensitivity and specificity); • Timing and/or sequence of tests when multiple tests are used; • Test threshold for positivity (e.g., positive test for HER2 is a immunohistochemistry [IHC] 3+ test defined as uniform intense membrane staining of >30% of invasive tumor); • Incorporation of the testing into the model structure. The consideration and impact of both parameter and structural uncertainty in relation to the above factors (where appropriate) was also assessed for each included study. Assessment of overall quality

Each included economic evaluation was also assessed for quality by two reviewers (BD and MT) using the QHES instrument developed by Chiou et al. [13]. The QHES instrument was chosen as it has been used previously to assess the quality of economic evaluations in other systematic reviews of genetic testing interventions [23–25]. The grading system has 16 criteria, each of which is associated with a specific value, such that the total quality score ranges from 0 to 100. Before grading the included studies, both reviewers reached consensus on the interpretation of the criteria. Studies were graded independently by both reviewers by deducting points from the maximum score of 100 for each criterion not addressed. The average of the two reviewers’ scores was used to obtain a final score. Synthesis of results

Primary and secondary syntheses were used to compare and contrast two different approaches of including companion diagnostics in a model-based economic evaluation. Differences in methodological characteristics of economic evaluations were also evaluated between the two groups of studies. The primary synthesis included economic evaluations comparing a doi: 10.1586/14737159.2014.929499


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companion diagnostic and subsequent treatment strategy to another alternative treatment strategy with model parameters for the sensitivity and specificity of the companion diagnostic included in the analysis. The secondary synthesis was strictly limited to economic evaluations of targeted therapies included in the primary synthesis, but restricted model parameters for the companion diagnostic to only its cost (i.e., did not link the testing strategy through to the impact on treatment decisions and final patient outcomes) and therefore did not include parameters for the sensitivity and specificity of the test in the model (whether this was explicitly mentioned in the study or not). A high-level tabular synthesis of the methodological characteristics from the included studies of both the primary and secondary syntheses was used to compare and contrast the overall quality of the evaluations according to standard metrics. The inclusion of test-related characteristics was also synthesized in tabular format for both the primary and secondary syntheses. This provided a comparison for the extent of inclusion and impact of these characteristics on overall model results (i.e., ICERs) for the two different approaches for including a companion diagnostic into a model-based economic evaluation of a targeted therapy. Results Study selection

The overall search identified a total of 2970 citations. After removing duplicates (n = 719), 2251 unique titles and abstracts were screened. Of these, 2117 were excluded and 134 were reviewed in full-text. A total of 30 studies were included in the review and the data were extracted (FIGURE 1). The agreement between the two reviewers for the full-text review was good (Cohen’s kappa = 0.881 standard error 0.033). Study characteristics Overview

The number of included studies in the review was fairly small despite the inclusion of 12 different oncology therapies in the search strategy; the 30 studies were for only 4 of the 12 therapies. This is in part due to the strict inclusion criteria of modeling the sensitivity and specificity of the diagnostic test being evaluated. Some of the excluded studies were economic evaluations of the other oncology therapies of interest (e.g., imatinib mesylate); however, these studies largely assessed the targeted therapy in a pre-defined genetic population, thus ignored the impact a diagnostic test can have on the overall cost-effectiveness finding. In addition, a number of the 12 oncology therapies have only recently received regulatory approval by the FDA and therefore it is unlikely that economic evaluation evidence will be currently available in the published literature. Primary synthesis

The primary synthesis included 12 studies for 4 test and therapy combinations. Two studies for cetuximab and KRAS testing doi: 10.1586/14737159.2014.929499

in colorectal cancer [26,27], three studies for crizotinib and ALK testing in lung cancer [28–30], one study for erlotinib and EGFR testing in lung cancer [31] and six studies for trastuzumab and HER2 testing in breast cancer [32–37]. TABLE 1 outlines some of the general characteristics of the model-based economic evaluations from the primary synthesis. The included studies were largely cost-utility analyses, applying a Markov model framework. Parameter uncertainty was most commonly assessed using univariate sensitivity analyses and half of the included studies used probabilistic sensitivity analysis to account for the uncertainty in a number of parameters simultaneously. However, only 42% of the studies expressed decision uncertainty in the form of a cost-effectiveness acceptability curve. Structural uncertainty was poorly assessed with only 42% of the included studies using scenario analyses. Since a number of ICERs for different comparisons are usually reported in studies, the ICERs for the comparison of the lowest cost testing strategy and respective targeted therapy compared with best supportive care or no testing and no targeted therapy was chosen as the most relevant ICER for comparison across studies. Thresholds of less than or equal to US$50,000/QALY, between US$50,000 and US$100,000/QALY and greater than US$100,000/QALY were used to group ICERs from the included studies as these are common thresholds cited to represent cost-effectiveness, possibly cost-effective and not costeffective, respectively [38]. The two lower thresholds each had 17% of the studies in their respective ranges, while a quarter of the studies had ICERs greater than US$100,000/QALY. It is important to note that five studies [27,29,31,36,37] did not report an ICER for the comparison of interest of our study and therefore could not be included in this comparison. Mean overall study quality as assessed by the QHES scale was 73/100, ranging from 59 to 87. Secondary synthesis

The secondary synthesis was used to provide a directly comparable group of studies to those included in the primary synthesis with the intent to contrast the approach to modeling a companion diagnostic. The secondary synthesis was strictly limited to evaluations that assessed a targeted therapy that had been included in the primary synthesis but had not included parameters for the sensitivity and specificity of the companion diagnostic and only incorporated its cost. The secondary synthesis included 18 studies for 4 test and therapy combinations. The number of studies for each combination was slightly larger (except for crizotinib) than that of the primary synthesis with: six studies for cetuximab and KRAS testing in colorectal cancer [39–44], one study for crizotinib and ALK testing in lung cancer [45], three studies for erlotinib and EGFR testing in lung cancer [46–48] and eight studies for trastuzumab and HER2 testing in breast cancer [49–56]. TABLE 2 outlines some of the general characteristics of the model-based economic evaluations from the secondary synthesis. Most of the included studies used cost-utility analyses (although a number also used cost-effectiveness analyses), Expert Rev. Mol. Diagn.

Database search Results to 24 February 2014 (n = 2066) Cochrane library (n = 82) EMBASE (n = 921) MEDLINE/HTA/NHSEED (n = 554) PubMed (n = 509)

Review

Grey literature and hand search Results to 27 February 2014 (n = 904) CADTH – HTA reports (n = 187) CADTH rapid responses (n = 551) PBAC/MSAC documents (n = 63) AHRQ Technology Assessments (n = 96) Hand searching (n = 7)

Screening

Duplicates removed (n = 719)

Eligibility

Records screened (n = 2251)

Included


Identification


Full-text studies assessed for eligibility (n = 134)

Records excluded (n = 2117)

Studies excluded (n = 104) Drug not of interest (n = 1) Editorial (n = 1) ICER not outcome measure (n = 1) Test not of interest (n = 1) Costing analysis (n = 2) EE of test only (n = 2) Not available (n = 2) Methods paper (n = 3) Test not modelled (n = 4) Duplicate publication (n = 5) Not genetically selected (n = 9) Not in English (n = 12) Trial-based EE (n = 13) Indication not of interest (n = 24) Test not modelled/no cost (n = 24)

Studies included in the primary synthesis (n = 12) Cetuximab (n = 2) Crizotinib (n = 3) Erlotinib (n = 1) Trastuzumab (n = 6) Studies included in the secondary synthesis (n = 18) Cetuximab (n = 6) Crizotinib (n = 1) Erlotinib (n = 3) Trastuzumab (n = 8)

Figure 1. Flow diagram for selection of studies. AHRQ: Agency for Healthcare Research and Quality; CADTH: Canadian Agency for Drugs and Technologies in Health; EE: Economic evaluation; HTA: Health Technology Assessment; ICER: Incremental cost-effectiveness ratio; PBAC: Pharmaceutical Benefits Advisory Committee; MSAC: Medical Services Advisory Committee.

applying a Markov model framework. Parameter uncertainty was most commonly assessed using univariate sensitivity analyses and over two-thirds of the included studies used probabilistic sensitivity analysis to account for the uncertainty in a informahealthcare.com

number of parameters simultaneously. However, only half of the studies expressed decision uncertainty in the form of a costeffectiveness acceptability curve. Structural uncertainty was also poorly assessed with roughly one-third of the included studies doi: 10.1586/14737159.2014.929499

doi: 10.1586/14737159.2014.929499

Type of model Univariate sensitivity analysis

MM – 2

2

AM – 1 MM – 1 NS – 1

2

DT – 1

1

AM – 2 (17%) DT – 2 (17%) MM – 7 (58%) NS – 1 (8%)

AM – 1 DT – 1 MM – 4

11 (92%)

6

4 (33%)

3

0

1

0

Multivariate sensitivity analysis

5 (42%)

1

1

2

1

Scenario analysis

Uncertainty analyses

6 (50%)

5

0

0

1

PSA

5 (42%)

4

0

0

1

CEAC

2 (17%)

2

0

0

0

ICERs† #US$50,000/ QALY

2 (17%)

1

0

0

1

ICERs‡ >US$50,000 #US$100,000/ QALY

Overall results (2013 $USD)

3 (25%)

1

0

2

0

ICERs§ >US$100,000/ QALY

73 (59–87)

75 (63–87)

64 (62–66)

69 (59–85)

78 (75–81)

QHES mean score (range)

§

‡

†

ICER £US$50,000/QALY or dominant for comparison of the lowest cost testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy. ICER >US$50,000 £US$100,000/QALY for comparison of the lowest cost testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy. ICER >US$100,000/QALY for comparison of the lowest cost testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy. { One study did not report an ICER for a testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy [27]. # One study did not report an ICER for a testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy [29] and it was assumed that the scenario for 1% biomarker frequency, screening cost of US$1400 and drug cost of US$10,000 would be the base case for the other study [28]. †† Study did not report an ICER for a testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy [31]. ‡‡ Two studies did not report an ICER for a testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy [36,37]. AM: Algebraic model; BC: Breast cancer; BSC: Best supportive care; CEA: Cost-effectiveness analysis; CEAC: Cost-effectiveness acceptability curve; CUA: Cost-utility analysis; DT: Decision tree; ICER: Incremental costeffectiveness ratio; MM: Markov model; NS: Not stated; PSA: Probabilistic sensitivity analysis.

CEA – 6 (50%) CUA – 10 (83%)

Overall (n = 12)

CEA – [33,34,37] CUA – [32–37]

Trastuzumab and HER2 testing (BC) (n = 6)‡‡

CEA – [31]

Erlotinib and EGFR testing (n = 1)††

CEA – [30] CUA – [28–30]

Crizotinib and ALK testing (n = 3)#

CEA – [27] CUA – [26]

Cetuximab and KRAS testing (n = 2){

Method of analysis

Table 1. Summary of the general characteristics of model-based economic evaluations (primary synthesis).


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Expert Rev. Mol. Diagn.

informahealthcare.com

Type of model Univariate sensitivity analysis

AUC – 1 MM – 4 MS – 1

AUC – 1 #

DT – 2 MM – 1

3

1

5

AM – 2 (11%) AUC – 2 (11%) DT – 4 (22%) MM – 9 (50%) MS – 1 (6%)

AM – 2 DT – 2 MM – 4

14 (78%)

5

1 (6%)

1

0

0

0

Multivariate sensitivity analysis

7 (39%)

4

1

1

1

Scenario analysis

Uncertainty analyses

12 (67%)

3

3

1

5

PSA

9 (50%)

1

3

1

4

CEAC

4 (22%)

2

0

0

2

ICERs† #US$50,000/ QALY

1 (6%)

1

0

0

0

ICERs‡ >US$50,000 #US$100,000/ QALY

5 (28%)

0

1

1

3

ICERs§ >US$100,000/ QALY

Overall results (2013 $USD)

74 (51–92)

68 (51–78)

78 (76–81)

84 (83–85)

79 (62–92)

QHES mean score (range)

§

‡

ICER £US$50,000/QALY or dominant for comparison of the lowest cost testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy. ICER >US$50,000 £US$100,000/QALY for comparison the lowest cost testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy. ICER >US$100,000/QALY for comparison the lowest cost testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy. { One study only reported an ICER for the comparison of interest using LYs not QALYs [42]. # Two studies did not report ICERs for a testing strategy and respective targeted therapy compared with BSC or no testing and no targeted therapy [46,47]. †† Two studies reported ranges of ICERs that crossed over multiple thresholds [49,51]; three studies also only reported ICERs for the comparison of interest using LYs not QALYs [50,53,54]. AM: Algebraic model; AUC: Area under the curve; BC: Breast cancer; BSC: Best supportive care; CEA: Cost-effectiveness analysis; CEAC: Cost-effectiveness acceptability curve; CUA: Cost-utility analysis; DT: Decision tree; ICER: Incremental cost-effectiveness ratio; LY: Life-year; MM: Markov model; MS: Micro-simulation; QALY: Quality-adjusted life-years.

†

CEA – 10 (56%) CUA – 12 (67%)

Overall (n = 18)

CEA – [49–54] CUA – [52,55,56]

Trastuzumab and HER2 testing (BC) (n = 8)††

CEA – [46] CUA – [46–48]

Erlotinib and EGFR testing (n = 3)

CUA – [45]

Crizotinib and ALK testing (n = 1)

CEA – [39,41,42] CUA – [39–41,43,44]

Cetuximab and KRAS testing (n = 6){

Method of analysis

Table 2. Summary of the general characteristics of model-based economic evaluations (secondary synthesis).



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doi: 10.1586/14737159.2014.929499


Review


using scenario analyses. To have a consistent appraisal to the primary synthesis, the ICERs for the comparison of the lowest cost testing strategy and respective targeted therapy compared with best supportive care or no testing and no targeted therapy was chosen as the most relevant ICER for comparison across studies. The same thresholds from the primary synthesis were also applied to group the studies according to their ICERs. Roughly one-quarter of the included studies had ICERs that were less than or equal to US$50,000/QALY. A small percentage of the studies (7%) had ICERs between US$50,000 and US$100,000/QALY and 28% of the studies reported ICERs greater than US$100,000/QALY. It should be noted that four studies [42,50,53,54] reported ICERs for the comparison of interest using LYs not QALYs as the outcome measure. If these results are included in the summary, 33% of the ICERs are less than or equal to US$50,000/LY or QALY, 11% are between US$50,000 and US$100,000/LY or QALY and 33% are greater than US$100,000/LY or QALY. In addition, two studies did not report an ICER for the comparison of interest of our study [46,47] and two studies reported ranges of ICERs that crossed over multiple thresholds [49,51] and therefore could not be included in this comparison. Mean overall study quality as assessed by the QHES scale was 74/100, ranging from 51 to 92. Comparison of study characteristics from the primary & secondary syntheses

As expected, the studies included in the primary synthesis (TABLE 1) tended to focus on the testing aspects of the analysis in comparison to the studies included in the secondary synthesis (TABLE 2) that largely focused on an evaluation of the therapeutic under study. In addition, three of the included studies from the primary synthesis [28,29,31] had a methodological focus (i.e., how best to incorporate testing into a model) rather than a focus on decision-making support as seen for the majority of the studies included in the secondary synthesis. Studies from the primary synthesis were also more recent with 75% of the studies published after 2010 in comparison to 50% for the secondary synthesis. Interestingly, the earliest study published [32] (i.e., in 2004 assessing HER2 testing and trastuzumab) was included in the primary synthesis and arguably provides one of the most comprehensive assessments of including testing strategies into an economic evaluation of a targeted therapy. However, it is obvious by the number of studies included in the secondary synthesis that these methods have not been consistently applied to subsequent economic evaluations of test and treat strategies. The differences in ICERs observed between the primary and secondary syntheses provide an insight into this; 17% of studies in the primary synthesis reported ICERs less than or equal to US$50,000/ QALY compared with 22–33% of studies in the secondary synthesis, suggesting that including the test’s sensitivity and specificity in a model increases the ICER. One study [30] provides a direct example of this as the ICER when incorporating ALK testing into the model for crizotinib is almost US doi: 10.1586/14737159.2014.929499

$5000 larger compared with the ICER for the treatment only in a pre-defined genetic population (i.e., ALK-positive patients). Parameter uncertainty was accounted for more consistently for studies included in the primary synthesis using univariate and multivariate sensitivity analyses; however, there was no difference in accounting for structural uncertainty using scenario analyses between studies from the primary and secondary syntheses. This may be problematic as incorporating a testing strategy into a model is likely to increase the uncertainty and therefore there should be an increased reliance on methods (e.g., scenario analyses) to account for this uncertainty. Inclusion of test characteristics TABLE 3 outlines the extent of the inclusion of various characteristics of companion diagnostic tests in studies from both the primary and secondary syntheses. These characteristics have been suggested to be of importance in the economic evaluation of companion diagnostics and their respective targeted therapy and possibly impact the overall cost-effectiveness results [6,10,11]. For studies included in the primary synthesis, the desired test characteristics were largely well reported. Notable exceptions to this were poor reporting of the specific type and manufacturer of the diagnostic test used in the evaluation (e.g., ALK (D5F3) XP, Cell Signaling Technology vs IHC test). It is important to specify the type and manufacturer of the test as the biochemical methods (e.g., type of antibody (5A4 or D5F3 or ALK1) used for an IHC test to identify ALK fusions) for detecting the genetic biomarker of interest may differ, potentially impacting the accuracy of the test in identifying patients appropriate for treatment. In addition, different types of tests may require unique tumor samples or have requirements for repeat testing that can have an impact on total testing costs. This may be one reason why so few studies included other testing-related costs (e.g., cost of tissue acquisition) in their models. Only one study [35] compared the difference of using multiple tests either in parallel or in sequence, despite seven other studies using multiple tests either assumed to be done in parallel [26] or in sequence [30,32–34,36,37]. Specific test thresholds for positivity were mentioned in only two studies [29,35] and limited to a discussion of the thresholds for the FISH tests. Inclusion of the testing strategy in the model schematic was also poorly represented in the majority of included studies. To improve transparency and understanding of how characteristics of companion diagnostics (e.g., sensitivity/specificity or sequencing of multiple tests) are being included in model-based economic evaluations, it may be helpful to depict testing strategies in model schematics as presented in FIGURE 2. As expected, studies included in the secondary synthesis largely failed to account for the desired test characteristics. When the test-related characteristics were assessed, they were often limited to only a few studies for each test and treatment combination. The impact of the test characteristics on the



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Respond to treatment True positive Positive test result

[+] No response to treatment

Receive targeted therapy

Death False positive [+]

First genetic test


Positive test result Negative test result

Population eligible for testing

Receive targeted therapy††

True positive False positive

Second genetic test† Negative test result

Receive standard treatment

True negative False negative

[+] [+] [+] [+]

Respond to treatment No test

Receive standard treatment

No response to treatment Death

Figure 2. Model schematic of a test and treat strategy of two hypothetical companion diagnostics in sequence compared with standard treatment. † The second genetic test could be either a test to detect the same genetic biomarker as the first genetic test (e.g., immunohistochemistry with FISH confirmation) or a test to detect a mutually exclusive genetic biomarker (e.g., first test detects EGFR mutations and second test detects ALK fusions). ‡ The targeted therapy may be the same as the one used after a positive test result from the first test (e.g., trastuzumab in the case of IHC with FISH confirmation) or a different targeted therapy when the second test detects a mutually exclusive genetic biomarker compared with the first genetic test (e.g., erlotinib for EGFR-positive patients and crizotinib for ALK-positive patients).

overall cost-effectiveness results were also poorly tested in sensitivity analyses. The main difference in methodology for including the companion diagnostic into the model for studies in the secondary synthesis is how the cost of testing is calculated. When modeling the sensitivity and specificity of the test (like studies included in the primary synthesis), the cost of testing can simply be assigned to patients at the termination points of the decision tree (FIGURE 2). However, since this portion of a model is absent in studies not including the sensitivity and specificity of the test (i.e., studies included in the secondary synthesis), other methods for assigning the cost of testing must be used. For a number of the studies included in the secondary synthesis, it was difficult to determine the actual methods used to calculate testing costs; however, when methods were reported the approaches varied. The most common approach was to divide the unit cost of the test by the prevalence of the genetic biomarker of interest, thereby determining the cost per patient to identify one patient with the genetic biomarker from the cohort of patients [43,45,49,52]. Other studies used more complex calculations that involved additional parameters such as the risk of developing metastatic disease to account for some patients being tested at diagnosis and eventually not receiving targeted therapy [44] as well as incorporating probabilities of repeat testing and non-informative secondary biopsies [48]. Other methods of only assigning testing costs to patients eventually receiving targeted therapy [53] or assigning costs to potential candidates informahealthcare.com

for targeted therapy [39,51] and assuming all patients received a test but allocating all the costs to the group of patients treated with the targeted therapy [50] were also used. Any justification for only incorporating the cost of the companion diagnostic and not considering the sensitivity and specificity of the test was also minimally reported in the included studies from the secondary synthesis. Reasons included: the probability of KRAS mutation status being incorrectly diagnosed to only be 0.14 [43,57] or 0%; [39]; and since one study [45] was a review and secondary analysis of a manufacturer’s submission to a health authority, the expert review group was unable to update the model to account for the possible impacts the test strategies could have on the efficacy and costeffectiveness of the targeted therapy (i.e., include the sensitivity and specificity of the test strategies). Thresholds for test positivity were also poorly reported with only one study stating a specific threshold [46]. Impact of test characteristics on model results

outline the impact of altering various companion diagnostic test characteristics in deterministic sensitivity analyses on the overall model results (i.e., ICERs) for the primary and secondary syntheses, respectively. The specific direction and magnitude of the change in the ICERs when altering these test characteristics is not always straight forward; however, some overall trends have been observed. First, altering the prevalence of the genetic biomarker of interest can impact the ICER,

TABLES 4 & 5

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3

1

6

12 (100%)

Crizotinib ALK (n = 3)

Erlotinib EGFR (n = 1)

Trastuzumab HER2 (n = 6)

Overall (n = 12)

1

3

6

14 (78%)

Crizotinib ALK (n = 1)

Erlotinib EGFR (n = 3)

Trastuzumab HER2 (n = 8)

Overall (n = 18)

7 (39%)

2

3

1

1

12 (100%)

6

1

3

2

Varied prevalence in sensitivity analysis

6 (33%)

1

2

1

2

4 (33%)

1

0

2

1

Type of test specified

12 (67%)

4

2

1

5

12 (100%)

6

1

3

2

Varied cost of test in sensitivity analysis

10 (56%)

5

1

1

3

NA

NA

NA

NA

NA

Stated method for calculating cost of test†

3 (17%)

1

1

1

0

2 (17%)

0

0

2

0

Included other testing related costs

NA

NA

NA

NA

NA

12 (100%)

6

1

3

2

Included sensitivity and specificity of test‡

NA

NA

NA

NA

NA

10 (83%)

6

0

2

2

Varied sensitivity and specificity in sensitivity analysis‡

3 (17%)

0

0

1

2

NA

NA

NA

NA

NA

Provided justification for using 100% sensitivity and specificity§

1 (6%)

NA

NA

1

0

2 (17%)

1

NA

1

0

Varied timing or sequence of multiple tests{

1 (6%)

0

1

0

0

2 (17%)

1

0

1

0

Stated a threshold for positive test#

3 (17%)

0

3

0

0

3 (33%)

2

1

1

0

Testing included in model structure

† Only applicable to the secondary synthesis and refers to the method of incorporating testing into a model in which only the cost of the test is considered and not its sensitivity and specificity (e.g., determine the cost per mutation positive patient identified using the cost of an individual test and the mutation prevalence). ‡ Only applicable to the primary synthesis as this was a necessary criterion for inclusion (i.e., any study not including parameters for the sensitivity and specificity of the companion diagnostic in the model was excluded from the primary synthesis). § Only applicable to the secondary synthesis. { Only applicable to studies that assessed two companion diagnostics for selecting targeted therapy (e.g., the use of IHC and FISH testing to select patients most appropriate to receive trastuzumab). # Studies must have reported thresholds for positivity in specific details (e.g., positive test for HER2 is a IHC 3+ test defined as uniform intense membrane staining of >30% of invasive tumor cells or a FISH test with a ratio of HER2 to CEP17 >2.2); it was not sufficient to report only minimal details of what constituted a positive test (e.g., positive test for HER2 is a IHC 3+ test). IHC: Immunohistochemistry; NA: Not applicable.

4

Cetuximab KRAS (n = 6)

Secondary synthesis

2

Cetuximab KRAS (n = 2)

Primary synthesis

Included prevalence of mutation

Table 3. Comparison of companion diagnostic characteristics from primary and secondary syntheses.


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Table 4. Impact of altering companion diagnostic test characteristics in deterministic sensitivity analyses on overall model results (primary synthesis).


Cetuximab and KRAS (n = 2)

Crizotinib and ALK (n = 3)

Erlotinib and EGFR (n = 1)

Trastuzumab and HER2 (BC) (n = 6)

Parameter

Number of studies with impacted ICER

Comments

Prevalence of mutation

1

One study showed that model results were most sensitive to the percentage of KRAS wild-type patients [27]

Cost of test

NE

Changes in test cost had little impact on ICERs

Test accuracy

NE

Changes in test accuracy had little impact on ICERs

Timing/ sequence of tests

NT

One study assumed the two tests (i.e., KRAS and BRAF) were done in parallel but did not test this assumption [26]. Other study only included one test [27]


3

Trend observed in three studies where decreasing ALK-positive prevalence rate increased ICERs [28–30] ICERs still remained high with higher ALK fusion frequencies due to high drug costs (i.e., more people are eligible for crizotinib) [30]

Cost of test

1

Changes in test cost resulted in large differences only at low prevalence rates (1%) but minimal differences at high prevalence rates (50%) [28]

Test accuracy

2

Impact of changing sensitivity and specificity only tested in two [29,30] of the three studies. Decreasing the specificity of a test in one study [29] by 1% increases total cost per patient by over US$200; whereas changing the specificity of the IHC test in the other study [30] slightly increased the ICER


1

Only using the more expensive test (i.e., FISH) increased the ICER, whereas only using the cheaper test (i.e., IHC) without FISH confirmation slightly decreased the ICER [30]


1

In scenario analysis using higher prevalence rate, similar response rates, higher treatment costs and better overall survival resulted in more favorable ICER for companion diagnostic strategy compared with targeted treatment with no testing

Cost of test

NE

Changes in test cost had little impact on ICERs

Test accuracy

NT

Test accuracy not varied in sensitivity analysis


NT

Studies only modeled one test


3

Trend observed in three studies where decreasing HER2-positive rate increased ICERs, whereas increasing HER2-positive rate decreased ICERs [34,35,37]

Cost of test

NE

Changes in test cost had little impact on ICERs. However, one study showed that the cost of FISH test would have to be US$1680 greater than the cost of IHC test to dramatically increase the ICER for the comparison of FISH testing alone vs IHC test followed by FISH confirmation of 2+ and 3+ results [32]

Test accuracy

6

Trend observed in five studies where increasing the sensitivity and/or specificity of IHC decreases the cost-effectiveness of FISH testing alone (i.e., most expensive testing strategy) compared with IHC test followed by FISH confirmation [32–36]. One study noted that the larger the price differential between two tests (e.g., IHC and FISH) the smaller the increase in sensitivity and specificity of the cheaper test (e.g., IHC) has to be to decrease the ICER [36]


1

Only one study included comparators for parallel FISH and IHC testing and sequential testing with FISH confirmation of IHC 2+ [35] Parallel FISH and IHC test was not cost-effective compared with FISH testing alone, whereas sequential testing was extendedly dominated by FISH testing alone (i.e., extended dominance is applied to remove from consideration strategies whose cost-effectiveness is inferior in comparison with at least one more expensive strategy). The other five studies limited their multiple test comparators to tests in sequence [32–34,36,37]

BC: Breast cancer; ICER: Incremental cost-effectiveness ratio; IHC: Immunohistochemistry; NE: Tested but no effect; NT: Not tested.


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Table 5. Impact of altering companion diagnostic test characteristics in deterministic sensitivity analyses on overall model results (secondary synthesis).


Cetuximab and KRAS (n = 6)

Crizotinib and ALK (n = 1)

Erlotinib and EGFR (n = 3)

Trastuzumab and HER2 (BC) (n = 8)

Parameter


Comments


NT

One study mentioned generally that parameter changes in deterministic sensitivity analyses did not strongly impact ICERs [41]. In the other five studies the KRAS prevalence rate was either not altered in sensitivity analysis [40,44] or not clear if its impact was tested [39,42,43]

Cost of test

NE

Changes in test cost had little impact on ICERs in five of the studies [39,41–44] and its impact was not tested in the other study [40]


NT

Only one study considered multiple tests (i.e., KRAS and BRAF) [42]; however, it was unclear if the tests were assumed to be in parallel or in sequence and this was not tested in sensitivity analysis


1

The impact of changing ALK positivity was dependent on the testing strategy used and it’s cost; however, a general trend was observed that as prevalence decreases, ICERs increase and that this is more pronounced the larger the cost of the test. It was also noted that the appropriate population to test (e.g., all patients with NSCLC or those with particular histological subtypes (adenocarcinoma) or patients who are also EGFR negative) is unknown and each of these sub-populations will have varying prevalence of ALK positivity. Testing the broadest population (e.g., all patients with NSCLC) minimizes missing patients who may benefit from crizotinib, but this is also the most expensive testing approach. By identifying the most appropriate enriched populations to be tested the costs of testing can be reduced, which will likely decrease the ICER [45]

Cost of test

NE

Changes in test cost had little impact on ICERs; removing test costs also only slightly decreased ICERs


NT

Uncertainty concerning the most appropriate timing for ALK testing was identified (e.g., parallel with EGFR testing at first-line or only when patients fail first-line treatment); however, it is unclear how these different approaches would affect ICERs. In addition, it was noted that if the model assumes testing takes place at diagnosis but patients do not receive targeted therapy until second line the testing costs associated with the patients who have died over this time period must be considered not just the testing costs of the patients who go on to receive targeted therapy


1

In one study, at low prevalence of EGFR mutation (1%) the clinically guided strategy was more effective but more expensive than the biologically guided strategy; when the mutation prevalence was high (30%) the biologically guided strategy was more effective but more expensive than the clinically guided strategy [47]. In the other two studies, changes in EGFR positivity either had little impact on ICERs [48] or were not discussed in results of sensitivity analysis [46]; however, in one study a trend was observed as prevalence got smaller, ICERs increased [48]

Cost of test

NE

Impact of varying test costs either had little impact on the ICERs [47] or were not discussed in results of sensitivity analysis [46,48]


NT

Studies only considered one test


NT

The impact of changing HER2 positivity only tested in relation to total costs (not ICER) in one study [51]. It was noted that higher HER2 positivity means more people are eligible for expensive treatments and therefore a larger impact on total standard costs for the model population. In another study, a range of HER2-positive prevalence values were used to determine the number of tests required to identify one positive patient [52]. In the other six studies, either the HER2-positive prevalence was not included in the model [53] or its impact was not tested in sensitivity analyses [49,50,54–56]

BC: Breast cancer; ICER: Incremental cost-effectiveness ratio; IHC: Immunohistochemistry; NE: Tested but no effect; NSCLC: Non-small-cell lung cancer; NT: Not tested.

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Table 5. Impact of altering companion diagnostic test characteristics in deterministic sensitivity analyses on overall model results (secondary synthesis) (cont.). Parameter


Comments

Cost of test

NE

Changes in test cost had little impact on ICERs for two studies [49,52]. The other six studies did not alter test costs in sensitivity analyses [50,51,53–56]


NT

Studies only considered one test (i.e., either FISH or IHC), with the exception of one study [52], which assigned patients to either FISH or IHC but never together

BC: Breast cancer; ICER: Incremental cost-effectiveness ratio; IHC: Immunohistochemistry; NE: Tested but no effect; NSCLC: Non-small-cell lung cancer; NT: Not tested.

particularly decreasing the prevalence can increase the ICER whereas increasing the prevalence may only moderately decrease the ICER, especially when drug costs are high. Second, the most appropriate patient population to receive the test should be identified as to reduce the total cost of testing and minimize missing patients who may benefit from the targeted therapy; this approach is likely to decrease the ICER. Third, the cost of the test seems to have little impact on the ICER as the unit cost per test is usually relatively small ranging from US$38 to US$1442; however, ICERs may be more sensitive to changes in test costs when the prevalence of the genetic biomarker of interest is small (e.g., 1%) and the cost of the test is in the high end of the range. Fourth, the impact of altering the sensitivity and specificity of a test can depend on the price differential between its cost and the cost of an alternative testing strategy (e.g., the larger the price differential between two alternative testing strategies [IHC and FISH], the smaller the increase in sensitivity and specificity of the cheaper test [IHC] has to be in order to decrease its ICER). Fifth, limited evidence exists concerning the impact on ICERs of using multiple tests in parallel or in sequence, especially in cases of using multiple tests that detect different genetic biomarkers of interest (e.g., EGFR and ALK). Sixth, the optimal timing of testing patients may also have to be considered (e.g., should patients be tested at diagnosis despite only being eligible to receive targeted therapy as second-line treatment?). Lastly, the impact of altering a test’s threshold for positivity on overall model results was not discussed or tested in sensitivity analysis in any of the included studies. This may be problematic as thresholds for positivity are often chosen based on clinical preference rather than discovered through empirical research, even though other thresholds may be available [58]. Ideally, multiple thresholds for positivity should be examined to identify the sensitivity and specificity pair at which the test is most cost-effective (i.e., the Optimal Operating Point) [6]. Once this point has been identified using a number of available methods [6,59,60], it can be entered into the economic evaluation. However, it should be noted that there is no consensus on the most appropriate methods for determining the Optimal Operating Point and its calculation can be complex. This maybe the reason why informahealthcare.com

the included studies only selected one threshold for positivity and simply used univariate or multivariate sensitivity analyses for the sensitivity and specificity parameters to overcome this limitation. Overall, a number of important considerations have been identified in our review as outlined above. It is apparent that further research is required to refine methods for incorporating the characteristics of companion diagnostics into model-based economic evaluations and to fully understand how different approaches can affect the cost-effectiveness of a test and treat strategy. To facilitate this research, a methodological checklist (TABLE 6) has been developed based on our findings of the potential impact altering various test-related characteristics can have on overall model results. In addition, the checklist draws on aspects of the modeling approaches used in comprehensive examples [30,32] identified in our review. The criteria outlined in our checklist should be considered when conducting any future economic evaluation of a companion diagnostic and targeted oncology therapy. Discussion Summary of evidence

This paper presents a systematic literature review of modelbased economic evaluations assessing both a companion diagnostic and a targeted oncology therapy. A total of 30 studies were included in the review (12 in the primary synthesis and 18 in the secondary synthesis). Despite the included studies from both the primary and secondary syntheses having similarly applied general methodological elements of an economic evaluation and scoring similar scores on a standard quality assessment tool, the incorporation of diagnostic test characteristics differed dramatically. This discrepancy may be problematic as we have shown that the reported ICERs may vary depending on the methodological approach of including a companion diagnostic into a model-based economic evaluation. Furthermore, we have highlighted that altering certain companion diagnostic test characteristics (e.g., prevalence of the genetic biomarker, specific population tested, type of test, test accuracy and timing/sequence of multiple tests) can have an impact on overall model results. To improve the reliability of conclusions drawn from model-based economic evaluations and to ensure doi: 10.1586/14737159.2014.929499

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Table 6. Checklist for including characteristics of companion diagnostics into economic evaluations. Yes

No

Has the testing strategy clearly been depicted in the presented model schematic?

£

£

Has the prevalence of the genetic biomarker(s) been specified and used to determine the proportion of patients to eventually receive the targeted therapy?

£

£

Has the impact of altering the prevalence of the genetic biomarker(s) been tested in both univariate and probabilistic sensitivity analyses?

£

£

Has the impact of using different testing populations where the prevalence of the genetic biomarker(s) are likely to vary been tested in sensitivity/scenario analyses?

£

£

Has the name/type of test been specified (e.g., ALK (D5F3) XP, Cell Signaling Technology)?

£

£

Has the cost of the test been reported; including currency and year?

£

£

Have other test-related costs been included and specifically identified?

£

£

Has the impact of altering testing costs been tested in both univariate and probabilistic sensitivity analyses?

£

£

Has the impact of altering the testing costs and prevalence rate been tested in multivariate sensitivity analyses?

£

£

Has the test accuracy (i.e., sensitivity and specificity) been incorporated into the model?

£

£

If test accuracy has not been incorporated, has appropriate justification been provided as to why test accuracy is 100%?

£

£

Have different test thresholds for positivity been reported in detail and used to determine the Optimal Operating Point (i.e., sensitivity and specificity pair at which the test is most cost-effective)?

£

£

If multiple tests are used, have different timings of the tests been assessed (i.e., in parallel or in sequence)?

£

£

If multiple tests are used, were different sequences of the tests assessed?

£

£

Has the impact of altering the timing of the test been considered (e.g., test given at initial diagnosis or before second-line treatment when test results dictate use of second-line therapy not first-line)?

£

£

Were scenario analyses used to account for parameter uncertainty in various test-related characteristics and structural uncertainty in the modeled testing strategy?

£

£

consistent reimbursement decisions are made for different test and treat strategies, standard methodological elements as outlined in our checklist (TABLE 6) should be followed for any future evaluations of companion diagnostics and targeted oncology therapies. Comparison with earlier reviews

A number of systematic reviews of economic evaluations of pharmacogenomic interventions across multiple therapeutic areas [23,24,61–65] or generally related to cancer therapies [25] or a certain type of cancer/targeted therapy [7,66] have been previously published. The focus of some of these reviews [23–25,61] has largely been to assess the overall quality of the evaluations. These studies largely conclude that economic evaluations of pharmacogenomic interventions are of reasonable quality. However, our review has shown that similar overall quality scores were obtained for studies included in both the primary and secondary syntheses using standard metrics [13], despite studies from the secondary synthesis using inappropriate methods for including a companion diagnostic into a model (i.e., consideration of only testing costs). This calls into question the usefulness of standard quality assessment tools for economic evaluations of

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companion diagnostics and targeted therapies and the need to consider additional quality criteria as outlined in our methodological checklist (TABLE 6). Other reviews [63–65] with a broad focus on the economic evaluation of pharmacogenomic interventions have highlighted the importance of including the prevalence of the genetic biomarker and the sensitivity and specificity of the genetic test in an economic evaluation. However, the impact of altering these parameters on ICERs is minimally discussed [64]. Our review builds on this concept as we have tried to compile a more comprehensive evidence base concerning the effects that altering various test-related characteristics can have on overall model results (TABLES 4 & 5). The importance of accounting for differences in test performance and genetic biomarker prevalence depending on the ethnicity of the patient population being tested has also been noted [63,65]. This was a factor that was not assessed in any of the included studies in our review. However, due to the potential impact that altering the prevalence of the genetic biomarker can have on cost-effectiveness results, it may be important to consider such differences (e.g., EGFR mutations in lung adenocarcinomas are seen in approximately 50% of Asians and 10% of non-Asians [67]).




In contrast to our review, Vegter et al. [62] identified the cost of the test to be a major cost driver, citing uncertainty in this parameter due to the large variation in reported unit test costs. Large variations were also observed in our review (ranging from US$38 to US$1442); however, we have shown that the impact of this parameter on overall results was minimal and was only an important parameter when the prevalence of the genetic biomarker was very small (1%) and the unit cost of the test was in the high range [28]. Two additional reviews of economic evaluations were also identified that specifically discussed genetic testing for selecting patients to receive targeted treatment with either trastuzumab using HER2 testing in breast cancer [7] or cetuximab using KRAS testing in metastatic colorectal cancer [66]. The review by Ferrusi et al. [7] provides a comprehensive review of all the economic evaluations assessing trastuzumab in breast cancer up until 2009. However, the conclusions from the review regarding the assessment and impact of HER2 testing are limited to only two studies [32,33]. Our review identified four additional studies [34–37] published since 2009 that have attempted to model HER2 testing and provides additional syntheses across multiple companion diagnostic and targeted therapy combinations. Despite the inclusion of additional evidence, the conclusions of our review are similar to those made by Ferrusi et al. Frank and Mittendorf [66] have provided a review of economic evaluations, in which genetic testing is conducted prior to pharmaceutical treatment in metastatic colorectal cancer. A number of issues consistent with the conclusions of our review were highlighted. Few studies considered the sensitivity and specificity of the genetic tests as well as additional factors that can affect test accuracy (e.g., biochemical method of the test or consistency of the laboratory conducting the test). As presented in our methodological checklist (TABLE 6), it will be important to consider these factors in any future economic evaluations. Differences in the methods of incorporating the cost of the test into the evaluation (e.g., no consideration as all patients receive the test, consideration only to patients who eventually receive the targeted therapy and consideration to all patients regardless of whether or not they receive targeted treatment) were also recognized. However, no attempt was made to identify the most appropriate approach. Our review highlights that the appropriate approach as a result of including the sensitivity and specificity of the test is to consider the cost of the test for all patients regardless of whether or not they receive targeted therapy. Furthermore, the need to account for testing costs of all patients due to the possibility of testing being conducted at diagnosis for identification of appropriate second-line treatment (i.e., not all patients will survive to or need second-line treatment) has been highlighted to avoid underestimating total testing costs [44,45]. Overall, our review is consistent with the conclusions of earlier reviews and has provided additional syntheses to support the importance of considering a number of test-related characteristics in economic evaluations of companion diagnostics and informahealthcare.com

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targeted therapies. Furthermore, we have attempted to show how different approaches to including these characteristics can impact the overall cost-effectiveness results and potentially alter a reimbursement decision. To ensure that consistent and appropriate methodological approaches are applied to future economic evaluations, we have also developed a methodological checklist (TABLE 6) to help guide the inclusion of companion diagnostic test characteristics into a model-based economic evaluation. Strengths & limitations

There are a number of strengths to this systematic review. A comprehensive search strategy was used, including grey literature sources to compile a complete resource of model-based economic evaluations of companion diagnostics and their associated targeted therapies in oncology. Reproducible methods and multiple reviewers were used to assess inclusion of studies as well as the quality of the economic evaluations. Accuracy in data extraction was also checked by a second reviewer in 25% of the included studies. Despite these strengths, this study has a few key limitations. First, the search strategy was limited to studies available in fulltext in the English language. A number of relevant studies may be published in other languages, which is apparent by the exclusion of 12 studies during full-text screening. In addition, to keep the review question and the number of initial citations manageable we decided to limit our focus to targeted therapies associated with companion diagnostics that have received FDA regulatory approval. Since regulatory approval is a prerequisite for reimbursement assessment, it is most likely that these companion diagnostic/targeted therapy combinations would have published economic evaluations. It may be possible that additional studies are published for other diagnostic tests/drug combinations that are not formally approved by the FDA or are currently under development that would be relevant for our review and discussion. Furthermore, our review only identified a small number of appropriate studies for the primary synthesis and to draw conclusions about best practice methods from such a small sample of evidence can be problematic. However, we still regard our synthesis as informative and future studies should consider the elements highlighted in our review (TABLE 6) when modeling companion diagnostics to provide more evidence of their impact on the cost-effectiveness of targeted therapies in oncology. Expert commentary

Our review has highlighted that there is variation in the application of methods for incorporating test-related characteristics of companion diagnostics into model-based economic evaluations of targeted therapies in oncology. Most problematic is the lack of incorporating the varying sensitivity and specificity of these tests and the potential impact of ignoring the possibility of improper diagnoses on overall cost-effectiveness results. In addition, the lack of considering doi: 10.1586/14737159.2014.929499


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how different test thresholds for positivity can impact the model parameters for the sensitivity and specificity of the test and possibly the overall model results is concerning. However, this also highlights the limited availability of easy to implement methods to address this problem and the need for further research in this area specifically related to genomic tests. Furthermore, the impact of altering the prevalence of the genetic biomarker, the specific population to be tested, the sequence and timing of testing and the inclusion of additional test-related costs should all be assessed in extensive sensitivity analyses to ensure the robustness of model results. We have also highlighted the limited ability of standard quality assessment tools [13] in differentiating high-quality economic evaluations of companion diagnostics that incorporate the impact of important test-related characteristics. Future studies assessing the economic value of companion diagnostics and their associated targeted therapies should consider our methodological checklist and look toward high-quality examples [30,32] existing in the literature to guide their methodological approach. To ensure consistent and appropriate reimbursement decision-making for both companion diagnostics and targeted therapies, it will be important to further refine methods for incorporating test-related characteristics into an economic evaluation. In addition, further understanding of their impact on overall model results will be important, especially as more complex diagnostic testing methods become more prominent in oncology (e.g., multiplex and next-generation sequencing technologies). Five-year view

Companion diagnostics approved by the FDA are limited in their ability to detect only a single biomarker of interest. As the technology surrounding genomic testing improves and the price of the technology decreases, the ability of clinicians to order multiple genetic tests, use panels of genetic markers and even whole tumor genome sequencing methods will increase dramatically [68]. This advancement will further individualize oncology treatment, but a number of issues will arise when trying to assess the economic value of these more complex diagnostic testing methods and how they relate to the use of a targeted therapy [69–72]. 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 [73]. Building on this, Longo et al. [74] 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 genetic

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biomarker) at the second administration of the test. This may impact the test performance and hence the value of the test. It will, therefore, be important to identify the monitoring cut-off (threshold) that optimizes the contribution of the test to population health in a given healthcare system. As clinicians begin to rely on multiple genomic markers and tests to select appropriate treatment (e.g., PIK3CA and TP53 mutations in HER2+ breast cancer) [75] and apply repeat testing to account for tumor heterogeneity [76], it will become increasingly important to consider these issues related to multiple tests in economic evaluations. The eventual application of whole tumor genome sequencing to select from a number of different targeted therapies in each individual poses a number of new challenges above and beyond those of assessing multiple diagnostic tests. These challenges have only been minimally discussed in the literature and may be due to a lack of cross-disciplinary knowledge [77]. Future research in this area will be required and the development of innovative evaluation frameworks outside the traditional model-based economic evaluation framework may need to be explored [78,79]. Combining test results into a genomic algorithm that assigns a score or probability to an event of clinical interest may be an alternative approach. However, this approach will require unique methods for value assessment [80] and will also have to consider the portability of the algorithms across populations. It will be important to increase communication across multiple disciplines (e.g., pathology, clinical oncology and health economics) involved in genomics research and clinical care to ensure appropriate data and methods are available for assessing the economic value of these more complex diagnostic testing methods and associated targeted therapies. Acknowledgements

B Doble is supported by research scholarships from Monash University. P Lorgelly is a recipient of a Victorian Government Translational Research Grant through the Victorian Cancer Agency. The funding sources had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review and approval of the manuscript or decision to submit the manuscript for publication. Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties. No writing assistance was utilized in the production of this manuscript.



Review

Key issues • The successful use of a targeted therapy is intrinsically linked to the ability of a companion diagnostic to correctly identify patients most likely to benefit from treatment. • This ability can be directly related to various characteristics of the companion diagnostic and therefore the economic value of these tests and targeted therapies should be assessed in combination. • Varying methodological approaches (e.g., consideration of the impact of the test on improper diagnoses or only considering the costs associated with testing) for incorporating a companion diagnostic into a model-based economic evaluation have been published in the literature. Expert Review of Molecular Diagnostics Downloaded from informahealthcare.com by Michigan University on 12/26/14 For personal use only.

• Incremental cost-effectiveness ratios may be smaller when only the cost of testing is considered in a model compared with additionally incorporating the impact of the test’s sensitivity and specificity on treatment decisions and therefore the former may be an inappropriate methodological approach. • Altering certain companion diagnostic test characteristics (e.g., prevalence of the genetic biomarker, specific population tested, type of test, test accuracy and timing/sequence of multiple tests) can have an impact on overall model results. • Similar overall quality scores were obtained for studies included in both the primary and secondary syntheses using standard metrics, despite studies from the secondary synthesis using inappropriate methods for including a companion diagnostic into a model (i.e., consideration of only testing costs). This calls into question the usefulness of standard quality assessment tools for economic evaluations of companion diagnostics and targeted therapies and the need to consider additional quality criteria. • Future studies assessing the economic value of companion diagnostics and their associated targeted therapies should consider our methodological checklist and look toward high-quality examples existing in the literature to guide their methodological approach. • Further research is required to refine methods for incorporating the characteristics of companion diagnostics into model-based economic evaluations and fully understand how different approaches can affect the cost-effectiveness of a test and treat strategy to ensure consistent and transparent reimbursement decisions are made. • The potential use of multiplex and next-generation sequencing testing further complicates the application of model-based economic evaluations for determining the value of a test and treat strategy in oncology. Further research from a multidisciplinary perspective will be required to ensure the appropriate economic assessment of these more complex diagnostic testing methods and associated targeted therapies.

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Aligning the economic value of companion diagnostics and stratified medicines.

Influence of companion diagnostics on efficacy and safety of targeted anti-cancer drugs: systematic review and meta-analyses.

Economic evaluations in gastroenterology in Brazil: A systematic review.

A Systematic Review of Health Economic Evaluations of Diagnostic Biomarkers.

Companion diagnostics for targeted cancer drugs - clinical and regulatory aspects.

Economic evaluations of implantable cardioverter defibrillators: a systematic review.

The methodological quality of health economic evaluations for the management of hip fractures: A systematic review of the literature.

Methodological developments in randomized controlled trial-based economic evaluations.

Companion diagnostics for the targeted therapy of gastric cancer.

What is the economic evidence for mHealth? A systematic review of economic evaluations of mHealth solutions.

Targeted Therapies in HER2-Positive Breast Cancer - a Systematic Review.

Development of Companion Diagnostics.

Systematic review, critical appraisal, and analysis of the quality of economic evaluations in stroke imaging.

Economic evaluations in bipolar disorder: a systematic review and critical appraisal.

Economic evaluations in kidney transplantation: frequency, characteristics, and quality-a systematic review.

B-cell lymphoma mutations: improving diagnostics and enabling targeted therapies.

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