Standardized Functional Verification

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Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. April Learn how and when to remove this template message. Categories : Electronic circuit verification. Namespaces Article Talk. Views Read Edit View history. By using this site, you agree to the Terms of Use and Privacy Policy. This process involves assessing the performance of the test in comparison with a 'gold standard' or reference test that is capable of assigning the sample status without error ie, a test that gives 'true' results. In simple terms, validation can be seen as a process to determine whether we are 'performing the correct test'.

In the field of medical genetics, with the almost complete absence of reference tests or certified reference materials, the reference should be the most reliable diagnostic method available. It is worth noting that the gold standard does not have to comprise results from a single methodology; different techniques could be used for different samples and in some cases the true result may represent a combination of results from a portfolio of different tests.

To avoid introducing bias, the method under validation must not, of course, be included in this portfolio. Validation data can be used to assess the accuracy of either the technology eg, sequencing for mutation detection or the specific test eg, sequencing for mutation detection in the BRCA1 gene. Generally speaking, the generic validation of a novel technology should be performed on a larger scale, ideally in multiple laboratories interlaboratory validation , and include a much more comprehensive investigation of the critical parameters relevant to the specific technology to provide the highest chance of detecting sources of variation and interference.

If a suitable performance specification is available, it is necessary to establish that the new test meets this specification within the laboratory; this process is called verification. In simple terms, verification can be seen as a process to determine that 'the test is being performed correctly'. Verification should usually be appropriate for CE-marked IVDD-compliant kits, but care should be taken to ensure that the performance specification is sufficient for the intended use of the kit, particularly with kits that are self-certified.

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Most diagnostic genetic tests are classified by the IVD directive as 'low-risk' and can be self-certified by the manufacturer without assessment by a third party. Other applications of verification may include a new test being implemented using a technology that is already well established in a laboratory eg, a sequencing assay for a new gene , or a test for which a suitable performance specification is available from another laboratory in which the test has already been validated.

In all cases, it is essential that laboratories obtain as much information as possible with regard to the validation that has been performed. The plan, experimental approach, results and conclusions of the validation or verification should all be recorded in a validation file, along with any other relevant details see the section 'Reporting the results'. In addition, the validation plan and outcome should be formally reviewed and approved.

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This paper is specifically focused on processes involved in analytical validation and verification of tests in human molecular genetics so as to provide working detail of the first component of the ACCE framework. Clock Gating Dynamic power reduction by gating the clock. Finally, what is the risk of faulty behavior? Any of these documents are subject to change and the results of interpretation must be updated as each document is updated. These precisely defined variables will in most cases be used to determine how to subject the target to variability — variability in the information it receives stimuli , variability in the conditions governing its operation while receiving the information, variability in the particulars of the logical operation as a consequence of its connectivity performed on the information, and variability in the particular sequencing as determined by the clocking of the logical operations. You include proven not accepted to distribute this download thomas. For this reason, it is essential that the sample profile be clearly detailed in the validation report, together with an analysis of how this relates to the factors considered critical to the performance of the test.

When reporting validations or verifications in peer-reviewed publications, it is strongly recommended that the STARD initiative Standards for Reporting of Diagnostic Accuracy 17 be followed as far as possible. Once a test validation has been accepted ie, the use and accuracy have been judged to be fit for the intended diagnostic purpose , it is ready for diagnostic implementation.

However, this is not the end of performance evaluation. The performance specification derived from the validation should be used to assess the 'validity' of each test run and this information should be added to the validation file at appropriate intervals. In many cases, the accumulation of data over time is an important additional component of the initial validation, which can be used to continually improve the assessment of test accuracy and quality.

Functional verification

The ongoing validation should include results of internal quality control, external quality assessment and nonconformities related to the test or technique as appropriate. The core aim of validation is to show that the accuracy of a test meets the diagnostic requirements.

Essentially, all tests are based on a quantitative signal, even if this measurement is not directly used for the analysis. Although measuring the proportion of a particular mitochondrial variant in a heteroplasmic sample is, for example, clearly quantitative, the presence of a band on a gel is commonly considered as a qualitative outcome. However, the visual appearance of the band is ultimately dependent on the number of DNA molecules that are present, even though a direct measurement of this quantity is rarely determined.

These differences in the nature of a test affect how estimates of accuracy can be calculated and expressed. For the purpose of this paper, we are concerned with two types of accuracy. Determining how close the fundamental quantitative measurement is to the true value is generally termed 'analytical accuracy'.

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However, it is often necessary to make an inference about the sample or the patient on the basis of the quantitative result. For example, if the presence of a band on a gel signifies the presence of a particular mutation, test results are categorized as either 'positive' or 'negative' for that mutation, on the basis of the visible presence of the band. Such results are inferred from the quantitative result, but are not in themselves quantitative. Determination of how often such a test gives the correct result is termed 'diagnostic accuracy'.

The term diagnostic accuracy is generally used to describe how good a test is at correctly determining a patient's disease status. The purpose of these guidelines is to enable laboratories to establish how good their tests are at correctly determining genotype; clinical interpretation of the genotype is not considered in this context. Therefore, for the purpose of this paper, the term diagnostic accuracy will be taken to relate exclusively to the ability of a test to correctly assign genotype irrespective of any clinical implication.

We distinguish three broad test types quantitative, categorical and qualitative that can be subdivided into five groups according to the method for interpreting the raw quantitative value to yield a meaningful result. The following sections discuss each of these test types in more detail and provide guidance on appropriate measurement parameters in each case. A summary of the characteristics of the different test types and examples is given in Table 2 , together with recommendations for appropriate measurement parameters and timing of validation.

For a quantitative test, the result is a number that represents the amount of a particular analyte in a sample. This can be either a relative quantity, for example, determining the level of heteroplasmy for a particular mitochondrial allele, or an absolute quantity, for example, measuring gene expression.

In either case, the result of a quantitative test can be described as continuous as it can be any number between two limits , including decimal numbers.

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Two components of analytical accuracy are required to characterize a quantitative test: trueness and precision. Typically, multiple measurements are made for each point and the test result is taken to be the mean of the replicate results excluding outliers if necessary. As quantitative assays measure a continuous variable, mean results are often represented by a regression of data a regression line is a linear average. Any deviation of this regression from the reference ie, the line where reference result equals test result indicates a systematic error, which is expressed as a bias ie, a number indicating the size and direction of the deviation from the true result.

There are two general forms of bias. With constant bias, test results deviate from the reference value by the same amount, regardless of that value. With proportional bias, the deviation is proportional to the reference value. Both forms of bias can exist simultaneously Figure 2. Types of bias. In each case, the broken line represents the perfect result in which all test results are equal to the reference.

Although measurement of bias is useful Figure 3 , it is only one component of the measurement uncertainty and gives no indication of how dispersed the replicate results are ie, the degree to which separate measurements differ.

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This dispersal is called precision and provides an indication of how well a single test result is representative of a number of repeats. Precision is commonly expressed as the standard deviation of the replicate results, but it is often more informative to describe a confidence interval CI around the mean result.

Performance characteristics, error types and measurement metrics used for quantitative tests adapted from Menditto et al Precision is subdivided according to how replicate analyses are handled and evaluated. Here, there is some variability in the use of terminology; however, for practical purposes, we recommend the following scheme based on ISO 20 and the International Vocabulary of Metrology: Repeatability refers to the closeness of agreement between results of tests performed on the same test items, by the same analyst, on the same instrument, under the same conditions in the same location and repeated over a short period of time.

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Repeatability therefore represents 'within-run precision'. Intermediate precision refers to closeness of agreement between results of tests performed on the same test items in a single laboratory but over an extended period of time, taking account of normal variation in laboratory conditions such as different operators, different equipment and different days. Intermediate precision therefore represents 'within-laboratory, between-run precision' and is therefore a useful measure for inclusion in ongoing validation.

Reproducibility refers to closeness of agreement between results of tests carried out on the same test items, taking into account the broadest range of variables encountered in real laboratory conditions, including different laboratories. Reproducibility therefore represents 'inter-laboratory precision'. In practical terms, internal laboratory validation will only be concerned with repeatability and intermediate precision and in many cases both can be investigated in a single series of well-designed experiments. Reduced precision indicates the presence of random error.

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The relationship between the components of analytical accuracy, types of error and the metrics used to describe them is illustrated in Figure 3. Any validation should also consider robustness, which, in the context of a quantitative test, could be considered as a measure of precision.

However, robustness expresses how well a test maintains precision when faced by a specific designed 'challenge', in the form of changes in preanalytic and analytic variables.

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Therefore, reduced precision does not represent random error. Typical variables in the laboratory include sample type eg, EDTA blood, LiHep blood , sample handling eg, transit time or conditions , sample quality, DNA concentration, instrument make and model, reagent lots and environmental conditions eg, humidity, temperature.

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Standardized Functional Verification describes the science of functional verification that applies to any digital hardware system. With a precise and. Editorial Reviews. From the Back Cover. Standardized Functional Verification describes the science of functional verification that applies to any digital hardware.

Appropriate variables should be considered and tested for each specific test. The principle of purposefully challenging tests is also applicable to both categorical and qualitative tests and should be considered in these validations as well.