Molecular diagnostics clinical utility strategy: a six-part framework

Frueh, Felix W.; Quinn, Bruce

doi:10.1586/14737159.2014.933075

Cited by 10 publications

(3 citation statements)

References 17 publications

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“…The most frequently used formats are the key questions format (12 frameworks) [ 7 , 11 – 15 , 20 – 23 , 32 , 41 , 42 ], the card format (five frameworks) [ 8 – 10 , 16 – 19 , 24 , 25 ], and the checklist format (two frameworks) [ 30 , 38 ]. The other frameworks have a less structured format and resemble general manuals (Table 2 ) [ 26 – 29 , 31 , 33 – 37 , 39 , 40 ].…”

Section: Resultsmentioning

confidence: 99%

How is genetic testing evaluated? A systematic review of the literature

et al. 2018

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Given the rapid development of genetic tests, an assessment of their benefits, risks, and limitations is crucial for public health practice. We performed a systematic review aimed at identifying and comparing the existing evaluation frameworks for genetic tests. We searched PUBMED, SCOPUS, ISI Web of Knowledge, Google Scholar, Google, and gray literature sources for any documents describing such frameworks. We identified 29 evaluation frameworks published between 2000 and 2017, mostly based on the ACCE Framework (n = 13 models), or on the HTA process (n = 6), or both (n = 2). Others refer to the Wilson and Jungner screening criteria (n = 3) or to a mixture of different criteria (n = 5). Due to the widespread use of the ACCE Framework, the most frequently used evaluation criteria are analytic and clinical validity, clinical utility and ethical, legal and social implications. Less attention is given to the context of implementation. An economic dimension is always considered, but not in great detail. Consideration of delivery models, organizational aspects, and consumer viewpoint is often lacking. A deeper analysis of such context-related evaluation dimensions may strengthen a comprehensive evaluation of genetic tests and support the decision-making process.

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Section: Resultsmentioning

confidence: 99%

How is genetic testing evaluated? A systematic review of the literature

et al. 2018

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“…This left a total of 20 studies that met our inclusion criteria and were subsequently abstracted. 5,7,[23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40] The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart is shown in Figure 1.…”

Section: First Stage: Targeted Systematic Literature Review and Identification Of The Components Of The Value Framework And Initial List mentioning

confidence: 99%

A Value Framework for the Assessment of Diagnostic Technologies: A Proposal Based on a Targeted Systematic Review and a Multistakeholder Deliberative Process in Latin America

et al. 2021

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Objectives: there are very few value frameworks (VFs) to assess health technologies that are focused on diagnostic tests; they usually do not reflect a multistakeholder process; and they are all developed in high-income countries. Our project performed a targeted systematic review, with the objective of proposing an evidence-based, up-to-date VF informed by a multinational multistakeholder group working in the health technology assessment (HTA) space.Methods: (1) A targeted systematic review, with the aim to identify existing VFs and their dimensions; and (2) generation a VF proposal through a mixed-methods, qualitative-quantitative approach.Results: From 73 citations identified, 20 met our inclusion criteria and served to provide the initial list of dimensions for our VF. An initial list of criteria and subcriteria for a preliminary VF was proposed. After a full-day deliberative face-to-face meeting with 30 relevant stakeholders from seven Latin American countries and the United Kingdom, the final VF was defined, consisting of 15 criteria: five "essential or core," six highly relevant, three moderately relevant, and one of low relevance. Barriers and facilitators of value assessment of diagnostic technologies were also discussed. Conclusions:We propose a VF oriented to diagnostic technologies based on a targeted systematic review and a participatory process with key HTA stakeholders. It is the first to be produced in a lower and middle income setting but can also be potentially useful in other contexts aimed to assist decision-making processes with these particularly complex health technologies.

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“…It builds on previous frameworks, including the United States Preventive Services Task Force approach for assessing preventive interventions (21); the Fryback–Thornbury hierarchy (22); the ACCE (Analytic Validity, Clinical Validity, Clinical Utility, and Ethical, Legal, and Social Implications) framework (23); the EGAPP (Evaluation of Genomic Applications in Practice and Prevention) standards (24); the Genetic testing Evidence Tracking Tool (GETT) (25); and the Frueh and Quin framework that aims to facilitate communication between test developers and health-technology evaluators (26). …”

Section: What Framework Should Guide Decision Making On the Use Of Bimentioning

confidence: 99%

Evidence-based medicine and big genomic data

Ioannidis

Khoury

2018

Human Molecular Genetics

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Genomic and other related big data (Big Genomic Data, BGD for short) are ushering a new era of precision medicine. This overview discusses whether principles of evidence-based medicine hold true for BGD and how they should be operationalized in the current era. Major evidence-based medicine principles include the systematic identification, description and analysis of the validity and utility of BGD, the combination of individual clinical expertise with individual patient needs and preferences, and the focus on obtaining experimental evidence, whenever possible. BGD emphasize information of single patients with an overemphasis on N-of-1 trials to personalize treatment. However, large-scale comparative population data remain indispensable for meaningful translation of BGD personalized information. The impact of BGD on population health depends on its ability to affect large segments of the population. While several frameworks have been proposed to facilitate and standardize decision making for use of genomic tests, there are new caveats that arise from BGD that extend beyond the limitations that were applicable for more simple genetic tests. Non-evidence-based use of BGD may be harmful and result in major waste of healthcare resources. Randomized controlled trials will continue to be the strongest arbitrator for the clinical utility of genomic technologies, including BGD. Research on BGD needs to focus not only on finding robust predictive associations (clinical validity) but also more importantly on evaluating the balance of health benefits and potential harms (clinical utility), as well as implementation challenges. Appropriate features of such useful research on BGD are discussed.

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Molecular diagnostics clinical utility strategy: a six-part framework

Cited by 10 publications

References 17 publications

How is genetic testing evaluated? A systematic review of the literature

How is genetic testing evaluated? A systematic review of the literature

A Value Framework for the Assessment of Diagnostic Technologies: A Proposal Based on a Targeted Systematic Review and a Multistakeholder Deliberative Process in Latin America

Evidence-based medicine and big genomic data

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