EGAPP™ Working Group Methods Summary
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The independent Evaluation of Genomic Applications in Practice and Prevention (EGAPP™) Working Group reviews the scientific evidence for selected genetic tests and develops recommendation statements about the appropriate use of these tests. The following tables provide summarized information about the methods that the EGAPP™ Working Group uses to conduct this work. These methods have also been published in January 2009 issue of Genetics in Medicine.
Table 1: Test Applications
This table describes categories of genetic test applications and some characteristics of how clinical validity and clinical utility are assessed for each.
Application | Clinical Validity | Clinical Utility |
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Diagnosis (symptomatic patient) | Association of marker with disorder |
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Disease Screening (asymptomatic patient) | Association of marker with disorder | Improved health outcome based on early intervention for screen positive individuals to identify a disorder for which there is intervention or treatment, or provision of information useful for personal or clinical decision making |
Risk assessment/susceptibility | Association of marker with future disorder (consider possible effect of penetrance) | Improved health outcomes based on prevention or early detection strategies |
Prognostic | Association of marker with natural history benchmarks of the disorder | Improved health outcomes, or outcomes of value to patients, based on changes in patient management |
Pharmacogenomic Predicting treatment response or adverse events | Association of marker with a phenotype/metabolic state that relates to drug efficacy or adverse drug reactions | Improved health outcomes or adherence based on drug selection or dosage |
Table 2: Components of Evidence Evaluation
Because of the newness of the field of genetic testing, direct evidence to answer an overarching question about the effectiveness and value of testing is rarely available. Therefore, the EGAPP™ Working Group evaluated evidence in three key areas to construct a chain of evidence to address the overarching question.
Analytic Validity (Technical Performance) |
Clinical Validity (Strength of Clinical Correlation) |
Clinical Utility (Impact on Health Outcomes) |
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Tests ability to accurately and reliably measure analyte or genotype of interest.
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Test’s ability to accurately and reliably identify or predict the disorder of interest.
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Likelihood that using the test to guide management will significantly improve health-related outcomes.
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Table 3: Quality of Evidence
This table describes how the quality of evidence was graded in terms of its adequacy to address the key questions of each of the evidence components: analytic validity, clinical validity, and clinical utility.
Note: The “levels” used in ranking studies are described in Table 4.
Table 4: Ranking of Data Sources/Study Designs for Components of Evaluation
This table illustrates how data sources and study designs were ranked in order to assess the quality of evidence for each component of the evidence evaluation.
Levela | Analytic Validity | Clinical Validity | Clinical Utility |
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1 |
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Meta-analysis of randomized controlled trials (RCT) |
2 |
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3 |
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4 |
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aHighest level is 1.
bA clinical decision rule is an algorithm leading to result categorization. It can also be defined as a clinical tool that quantifies the contributions made by different variables (e.g., test result, family history) in order to determine classification/interpretation of a test result (e.g., for diagnosis, prognosis, therapeutic response) in situations requiring complex decision-making.
Table 5: Recommendation Classification
This table demonstrates the possible recommendations derived from the evaluation of the evidence components, the overall level of certainty of net health benefits, and contextual factors.
Level of Certainty | Recommendation |
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High or Moderate |
Recommend for . . . Recommend against . . . |
Low | Insufficient evidence . . . . . . if the evidence for clinical utility or clinical validity is insufficient in quantity or quality to support conclusions or make a recommendation. |
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- Page last reviewed: October 21, 2011 (archived document)
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