Evidence about the health economic outcomes of a diagnostic test is often lacking and has been mentioned as a common reason for diagnostics failing to obtain appropriate coverage. |
Evaluating the cost effectiveness of diagnostic biomarkers is challenging because diagnostics themselves do not influence long-term outcomes directly, but rather impact on therapeutic decisions and the subsequent care process. |
Economic evaluations on diagnostic biomarkers typically require comprehensive models to deal with all possible test–treatment combinations in various populations to assess their value in terms of health economic outcomes. |
More effort should be made to align the choice of health economic evaluation designs and outcomes with the actual information needs of the various public and private payers and care provider decision makers. |
Incorporating the results of non-health outcomes and patient preferences and improving the evidence base of other input parameters is crucial to fully capture the potential value of diagnostic biomarkers. |
1 Background
2 Methods
2.1 Search Strategy
2.2 Study Selection
2.3 Data Extraction
3 Results
3.1 Study Selection
3.2 General Study Characteristics
Study | Year | Countrya
| Disease | Population | Target audience | Comparisons | Type of biomarker |
---|---|---|---|---|---|---|---|
Behl [40] | 2012 | United States | Metastatic colorectal cancer | Patients (metastatic colorectal cancer, guide treatment) | Policymakers, payers | 3 strategies compared to reference | KRAS and BRAF mutation testing |
Breijer [20] | 2012 | The Netherlands | Endometrial carcinoma | Patients (suspected endometrial carcinoma) | NR | 4 defined strategies: compared 3 strategies to reference | Diagnostic strategies based on multivariable prediction models including clinical characteristics |
Bruno [21] | 2013 | Italy | Lung cancer | Patients (suspected lung cancer) | NR | 2 strategies (with/without) | Cytological examination (rapid on site) |
Castelao Naval [36] | 2013 | Mexico | Bronchogenic carcinoma | Patients (diagnosis bronchogenic carcinoma, staging) | NR | 2 strategies (with/without) | Transbronchial needle aspiration |
Collinson [22] | 2013 | United Kingdom | Acute Myocardial Infarction (AMI) | Patients (suspected myocardial infarction) | NR | 4 strategies compared to reference (no testing) | Cardiac biomarker assays: highly sensitive troponin assays and range of biomarkers (for plaque destabilization, myocardial ischemia and necrosis) |
Covarelli [37] | 2012 | Italy | Malignant melanoma | Patients (diagnosis malignant melanoma, staging) | NR | 2 strategies | Sentinel node biopsy under local or general anesthesia |
Fitzgerald [23] | 2011 | United Kingdom | Myocardial infarction | Patients (suspected myocardial infarction) | NR | 2 strategies | Assays of cardiac biomarkers (CK-MB, myoglobin, troponin) under point-of-care testing or standard strategy |
Gani [24] | 2011 | Saudi Arabia | Intra-abdominal cancer | Patients (suspected abdominal lesions) | NR | 2 strategies compared to reference (standard of care) | Ultrasound-guided percutaneous fine needle aspiration |
Gausachs [25] | 2012 | Spain | Lynch syndrome (colorectal cancer) | Patients (suspected genetic predisposition of colorectal cancer) | Decision makers | 3 strategies (2 strategies compared to least cost-effective) | BRAF V600E mutation testing, MLH1 promoter hypermethylation |
Heilbrun [26] | 2012 | United States | Renal cancer | Patients (suspected renal cell carcinoma) | Decision makers, management of hospitals | 2 strategies | Percutaneous biopsy or active surveillance of solid renal mass |
Heller [27] | 2012 | United States | Thyroid cancer | Patients (suspected thyroid cancer) | NR | 2 strategies | Repeating fine needle aspiration (cells of undetermined significance), or diagnostic lobectomy |
Kwon [28] | 2010 | United States | Breast cancer | Patients (diagnosis breast cancer, suspected BRCA mutation) | Decision makers on a national health service level | 5 strategies compared to reference (no testing) | BRCA genetic testing strategies (variable populations) |
Kwon [29] | 2011 | United States | Endometrial cancer | Patients (diagnosis endometrial cancer, suspected Lynch syndrome) | Decision makers on a national health service level | 6 strategies compared to reference | Genetic testing strategies (variable populations) |
Ladabaum [47] | 2011 | United States | Colorectal cancer | Patients + relatives (diagnoses colorectal cancer, genetic testing) | Decision makers on a national health service level | 16 strategies compared to reference | Genetic testing criteria (clinical or tumor testing; up-front germline testing) |
Lala [41] | 2013 | United States | Acute coronary syndrome | Patients (acute coronary syndrome, guide treatment) | Clinicians, decision makers | 3 strategies: genetic testing compared to therapy 1 and therapy 2 | CYP2C19 genetic testing to guide antiplatelet therapy |
Leiro Fernandez [30] | 2012 | Spain | Peripheral lung lesions | Patients (suspected lung lesions) | NR | 2 strategies (with/without) | Transbronchial needle aspiration in addition to transbronchial biopsy |
Li [38] | 2011 | United States | Thyroid cancer | Patients (diagnosis thyroid cancer, staging) | Clinicians, national decision makers | 2 strategies | Preoperative molecular classification (of indeterminate biopsy results) |
Mvundura [48] | 2010 | United States | Lynch syndrome (colorectal cancer) | Patients + first-degree relatives (diagnosis colorectal cancer, genetic testing) | Public and private health decision makers, laboratory directors | 4 strategies with 3 applications, resulting in 12 estimates | Genetic testing (genetic sequencing, IHC testing, BRAF testing, microsatellite instability testing) |
Nherera [49] | 2011 | United Kingdom | Familial hypercholesterolemia | Patients + relatives (suspected familial hypercholesterolemia) | NR | 3 strategies compared to reference (cholesterol measurement) | Genetic testing (definite, possible FH-mutation cases) |
Oppong [42] | 2013 | Norway and Sweden | LRTI | Patients (respiratory tract infection, guide treatment) | Decision makers on a national health service level | 2 strategies (yes/no) | Point-of-care C-reactive protein testing |
Panattoni [43] | 2012 | New Zealand | Acute coronary syndrome | Patients (acute coronary syndrome, guide treatment) | Decision makers on a national health service level | 3 strategies: genetic testing vs. therapy 1 and therapy 2 (3 comparisons) | CYP2C19 genetic testing to guide treatment |
Perez [50] | 2011 | United States | Congenital long-QT syndrome | Patients + first-degree relatives (long-QT syndrome, genetic testing) | NR | 2 strategies compared to reference (watchful waiting) | Genetic testing (LQTS mutation identified in patient) |
Retel [44] | 2012 | The Netherlands | Breast cancer | Patients (diagnosis breast cancer, guide treatment) | NR | 2 strategies | 70-gene signature gene expression profiling to guide chemotherapy treatment |
Romanus [45] | 2015 | United States | NSCLC | Patients (newly diagnosed metastatic NSCLC, guide treatment) | NR | 3 strategies compared to reference | EFGR mutation testing to guide treatment |
Sharma [51] | 2012 | United Kingdom | Familial hypercholesterolemia | Patients + relatives (familial hypercholesterolemia, genetic testing) | Decision makers on a national health service level | 11 strategies compared to reference (cholesterol measurement) | Elucigene FH20, LIPOchip targeted testing, comprehensive genetic testing, cholesterol measurement |
Shaw [31] | 2011 | United Kingdom | Acute kidney injury after cardiac surgery | Patients (suspected acute kidney injury) | Clinicians | 2 strategies (with/without) | Urinary NGAL biomarker |
Shiroiwa [46] | 2010 | Japan | Metastatic colorectal cancer | Patients (diagnosis metastatic colorectal cancer, guide treatment) | Decision makers on a national health service level | 3 strategies (3 comparisons) | Genetic testing of KRAS mutation to guide cetuximab treatment |
Steinfort [32] | 2013 | Australia | Peripheral lung lesions | Patients (suspected peripheral lung lesions) | Clinicians | 2 strategies | Endobronchial ultrasound-guided transbronchial lung biopsy, computed tomography-guided percutaneous needle biopsy |
Vanni [33] | 2011 | Brazil | Cervical neoplasms | Patients (suspected cervical neoplasms) | NR | 4 strategies compared to reference | Management strategies including HPV DNA testing |
Verry [39] | 2012 | Australia | Breast cancer | Patients (diagnosis breast cancer, staging) | Clinicians | 2 strategies | Sentinel lymph node biopsy, axillary lymph node dissection |
Wordsworth [52] | 2010 | United Kingdom | HCM | Patients + first-degree relatives (HCM, genetic testing) | NR | 4 strategies compared to reference | Genetic testing HCM mutation |
Yip [34] | 2012 | United States | Thyroid cancer | Patients (suspected thyroid nodule) | NR | 2 strategies (with/without) | Molecular testing of fine-needle aspiration |
Zanocco [35] | 2013 | United States | Thyroid cancer | Patients (thyroid neoplasm, suspected of malignancies) | NR | 2 strategies compared to reference | Intraoperative pathology examination during diagnostic hemithyroidectomy |
3.3 Methodological Characteristics
Study | Type | Model | Perspective | Horizon | Type evaluation | Outcome measures | Scenario analyses | Deterministic SA | Probabilistic SA |
---|---|---|---|---|---|---|---|---|---|
Behl [40] | Model based | Markov model | Healthcare system perspective | 10 years | CEA | Costs/life-year gained | No | Yes | Yes |
Breijer [20] | Model based | Decision tree | Healthcare system perspective | 5 years | CEA | Cost per extra patient surviving at 5 years | No | No | No |
Bruno [21] | Trial based | Decision tree | Hospital perspective (costs of test only) | Duration of diagnostic process | CMA | Cost savings | No | No | No |
Castelao Naval [36] | Trial based | NA | Hospital perspective (costs of test only) | Duration of diagnostic process | CMA | Cost savings | No | No | No |
Collinson [22] | Model based | Decision tree | Health service perspective | Lifetime horizon | CUA | Cost/QALY | No | Yes | Yes |
Covarelli [37] | Trial based | Decision tree | Operating room perspective | Surgery duration | CMA | Cost savings | No | No | No |
Fitzgerald [23] | Trial based | NA | Healthcare system perspective (NHS) | 3 months | CUA | Cost/QALY | No | Yes | No |
Gani [24] | Trial based | NA | Hospital perspective | 2 years | CMA | Cost savings | No | No | No |
Gausachs [25] | Trial based | Decision tree | Healthcare system perspective | Duration of diagnostic process | CEA | Cost per additional case detected | No | Yes | No |
Heilbrun [26] | Model based | Decision tree and Markov model | Third-party payer perspective | Lifetime horizon | CEA and CUA | Costs/QALY; costs/life-year gained | Yes | Yes | No |
Heller [27] | Model based | Decision tree and Markov model | Third-party payer perspective | Lifetime horizon | CUA | Costs/QALY | No | Yes | Yes |
Kwon [28] | Model based | Markov model | Societal perspective | Lifetime horizon | CEA and CUA | Costs/QALY; costs/life-year gained | Yes | Yes | Yes |
Kwon [29] | Model based | Markov model | Societal perspective | Lifetime horizon | CEA | Costs/life-year gained | Yes | Yes | Yes |
Ladabaum [47] | Model based | Decision tree and Markov model | Third-party payer perspective | Lifetime horizon | CEA | Costs/life-year gained | No | Yes | Yes |
Lala [41] | Model based | Decision tree and Markov model | Third-party payer perspective | 5 years and 10 years | CUA | Costs/QALY | Yes | Yes | Yes |
Leiro Fernandez [30] | Trial based | NA | Hospital perspective | Hospital stay | CEA | Cost per additional case detected | No | Yes | No |
Li [38] | Model based | Markov model | Healthcare system perspective | 5 years | CUA | Costs/QALY | No | Yes | Yes |
Mvundura [48] | Model based | Decision tree | Healthcare system perspective | Lifetime horizon | CEA and CUA | Costs/life-year gained; costs/QALY | No | Yes | No |
Nherera [49] | Model based | Decision tree and Markov model | Healthcare system perspective (NHS) | Lifetime horizon | CUA | Costs/QALY | No | Yes | Yes |
Oppong [42] | Trial based | NA | Health service perspective | 4 weeks | CEA and CUA | Cost per patient reduction in prescribed antibiotics; costs/QALY; net monetary benefit | No | No | Yes |
Panattoni [43] | Model based | Short-term: decision tree; long-term: Markov model | Healthcare system perspective | Short-term: 15 months; long-term: lifetime | CUA | Costs/QALY | No | No | Yes |
Perez [50] | Model based | Decision tree and Markov model | Societal perspective | 60 years | CEA and CUA | Costs/QALY; costs/life-year gained | No | Yes | No |
Retel [44] | Model based | Markov model | Healthcare system perspective | 20 years | CUA | Costs/QALY | No | No | Yes |
Romanus [45] | Model based | Markov model | Societal perspective | 2 years | CEA and CUA | Costs/QALY; costs/life-year gained | No | Yes | No |
Sharma [51] | Model based | Decision tree and Markov model | Healthcare system perspective (NHS) | Lifetime horizon | CUA | Costs/QALY | No | Yes | Yes |
Shaw [31] | Model based | Decision tree | Healthcare system perspective (NHS) | Lifetime horizon | CUA | Costs/QALY | No | Yes | Yes |
Shiroiwa [46] | Model based | Markov model | Third-party payer perspective | 2.5 years | CEA and CUA | Costs/QALY; costs/life-year gained | No | Yes | Yes |
Steinfort [32] | Model based | Decision tree | Hospital perspective | Duration of diagnostic process | CUA and CMA | Costs/QALY; cost savings | No | Yes | Yes |
Vanni [33] | Model based | Markov model | Healthcare system perspective | Lifetime horizon | CEA | Costs/life-year gained | No | Yes | Yes |
Verry [39] | Model based | Markov model | Healthcare system perspective | 20 years | CUA | Incremental costs and incremental QALYs | Yes | Yes | No |
Wordsworth [52] | Model based | Decision tree and Markov model | Hospital perspective | Lifetime horizon | CEA | Costs/life-year gained | No | Yes | Yes |
Yip [34] | Model based | Decision tree | Hospital perspective | Duration of diagnostic process | CEA | Costs per each additional case detected | Yes | Yes | No |
Zanocco [35] | Model based | Markov Model | Societal perspective | Lifetime horizon | CUA | Costs/QALY | Yes | Yes | Yes |
3.4 Type of Health Economic Evaluation
3.5 Perspective and Time Horizon of the Analysis
3.6 Decision Model
3.7 Thresholds
3.8 Sensitivity Analyses
3.9 Handling of Specific Issues
Issue |
N
| References | Solutions |
---|---|---|---|
Variable framing of questions due to different payer perspectives | 0 | ||
Variable framing of questions due to several clinical perspectives | 1 | [46] | (1) Regarding companion diagnostic; (2) regarding therapeutic strategy |
Multiple subpopulations according to test sequence and applications in several therapeutic areas | 7 | Bivariate analyses, varied in deterministic and probabilistic sensitivity analysis, particularly with regard to cascade screening (number of relatives), age-targeted screening, and multiple ethnicities | |
Multiple realistic strategies (computationally heavy) due to sequences of testing and rapid evolution of clinical pathways | 19 | Multiple comparisons (up to N = 16) due to multiple technologies/combinations of tests/treatment options, also included in scenario analyses | |
Sensitivity of effect estimates to adherence and compliance to a test | 7 | Proportion of compliance with diagnosis/accepting counseling are varied in deterministic and probabilistic sensitivity analyses or recognized by defining a reference case scenario populated with scientific literature | |
Preference heterogeneity in valuing outcomes (population versus patients) | 1 | [31] | Incorporated as disutility in terms of ‘wait-trade-off’a
|
Opportunity cost of tests (depending on the number of tests performed, geography and population density variability) | 0 | ||
Cost-sharing arrangements between producers and payers | 0 |