Evaluating genomic tests from bench to bedside: a practical framework

• Published in: BMC medical informatics and decision making Vol. 12; no. 1; p. 117
• Main Authors: Lin, Jennifer S, Thompson, Matthew, Goddard, Katrina A B, Piper, Margaret A, Heneghan, Carl, Whitlock, Evelyn P
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 19.10.2012; BioMed Central; BMC
• Subjects: Clinical medicine; Colorectal cancer; Correspondence; Decision Making; Deoxyribonucleic acid; Diagnostic test; DNA; Evaluation framework; Evidence-based decision making; Evidence-Based Medicine; Genetic screening; Genetic Testing - standards; Genetic/genomic; Genomics; Health sciences; Humans; Medical research; Medical tests; Medicine; Patients; Prognostic test; Regulation of financial institutions; Regulatory approval; Reproducibility of Results; Risk assessment; Test development; Validity
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/1472-6947-12-117

The development of genomic tests is one of the most significant technological advances in medical testing in recent decades. As these tests become increasingly available, so does the need for a pragmatic framework to evaluate the evidence base and evidence gaps in order to facilitate informed decision-making. In this article we describe such a framework that can provide a common language and benchmarks for different stakeholders of genomic testing. Each stakeholder can use this framework to specify their respective thresholds for decision-making, depending on their perspective and particular needs. This framework is applicable across a broad range of test applications and can be helpful in the application and communication of a regulatory science for genomic testing. Our framework builds upon existing work and incorporates principles familiar to researchers involved in medical testing (both diagnostic and prognostic) generally, as well as those involved in genomic testing. This framework is organized around six phases in the development of genomic tests beginning with marker identification and ending with population impact, and highlights the important knowledge gaps that need to be filled in establishing the clinical relevance of a test. Our framework focuses on the clinical appropriateness of the four main dimensions of test research questions (population/setting, intervention/index test, comparators/reference test, and outcomes) rather than prescribing a hierarchy of study designs that should be used to address each phase.