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High-Bandwidth Memory HBM A dense, stacked version of memory with high-speed interfaces that can be used in advanced packaging. IC Types. Impact of lithography on wafer costs Wafer costs across nodes. Implementation Power Optimizations Power optimization techniques for physical implementation. Induced Gate Noise Thermal noise within a channel. Integrated Circuits ICs Integration of multiple devices onto a single piece of semiconductor.
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Logic Resizing Correctly sizing logic elements. Logic Restructuring Restructuring of logic for power reduction. Logic Simulation A simulator is a software process used to execute a model of hardware. Low Power. Low Power Methodologies Methodologies used to reduce power consumption.
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Mixed-Signal The integration of analog and digital. Models and Abstractions Models are abstractions of devices.
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Power Switching Controlling power for power shutoff. Power Techniques. Power-Aware Design Techniques that analyze and optimize power in a design. Power-Aware Test Test considerations for low-power circuitry. Process Power Optimizations power optimization techniques at the process level.
Process Variation Variability in the semiconductor manufacturing process. Processor Utilization A measurement of the amount of time processor core s are actively in use. Property Specification Language Verification language based on formal specification of behavior. Quantum Computing A different way of processing data using qubits. Random Telegraph Noise Random trapping of charge carriers.
Recurrent Neural Network RNN An artificial neural network that finds patterns in data using other data stored in memory. Reliability Verification Design verification that helps ensure the robustness of a design and reduce susceptibility to premature or catastrophic electrical failures. RVM Verification methodology based on Vera. SAT Solver Algorithm used to solve problems. Scan Test Additional logic that connects registers into a shift register or scan chain for increased test efficiency.
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.
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. 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. 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.
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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'. 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. 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. 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.
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. The relationship between the components of analytical accuracy, types of error and the metrics used to describe them is illustrated in Figure 3.
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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. 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.
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. Robustness can be considered as a useful prediction of expected intermediate precision. As trueness and precision represent two different forms of error, they need to be treated in different ways.
In practice, systematic error or bias can often be resolved by using a correction factor; constant bias requires an additive correction factor, whereas proportional bias requires a multiplicative correction factor. Random error, in contrast, cannot be removed, but its effects can generally be reduced to acceptable levels by performing an appropriate number of replicate tests. For the purpose of this paper, a basic understanding of the concepts described above is the main objective.
However, it is worth outlining some of the complexities that can arise in estimating the analytical accuracy of quantitative tests. In molecular genetics, quantitative measurements are most often relative, that is, two measurements are taken and the result is expressed as a proportion eg, the percentage of heteroplasmy of a mitochondrial mutation. In such cases, it is preferable to perform both measurements in a single assay to minimize the effects of proportional bias, as the assay conditions are likely to affect both the measurements in a similar way. If the measurements must be taken in separate assays, each measurement is effectively an absolute measurement and must be quantified in comparison with a set of calibration standards run with each test batch.
This is most effectively achieved by monitoring the efficiencies of the two reactions over time. For quantitative tests, particularly those requiring absolute quantification, it is most effective to estimate analytical accuracy on an ongoing basis by running a set of calibration standards standard curve with each batch or run. In this case, it is important that linearity be evaluated 24 and that the lower and upper standards are respectively below and above the expected range of the results as precision cannot be assessed on extrapolated results.
Where possible, calibration standards should be traceable to absolute numbers or to recognized international units. Other factors that may need to be evaluated include the limit of detection defined as the lowest quantity of analyte that can be reliably detected above background noise levels and the limits of quantification that define the extremities at which the measurement response to changes in the analyte remains linear.
It should be noted that limit of detection is sometimes referred to as 'sensitivity'; that is, how sensitive a methodology is to detecting low levels on a particular analyte in a large background. Use of the term 'sensitivity' in this context should be avoided, as it may be confused with sensitivity described in the section 'Qualitative tests' ie, the proportion of positive results correctly identified by a test. It can be seen that the analysis of all but the simplest quantitative assays can be complex and it is recommended that statistical advice be sought to determine those factors that need to be measured and the best way to achieve it.
Categorical tests sometimes referred to as semiquantitative 26 are used in situations in which quantitative raw data, which could have any value including decimals, are grouped into categories to yield meaningful results. For example, fluorescent capillary analysis might be used to determine the size of PCR products in base pairs by analysing the position of the peaks relative to an internal size standard. The quantitative results from this analysis will include numbers with decimal fractions, but the length of the product must be a whole number of base pairs; a fragment cannot be Therefore cutoffs must be used to assign quantitative results to meaningful categories.
The parameters used to describe the estimates of analytical accuracy for a quantitative test Figure 3 can be used to describe the performance of the categorical test in much the same way. However, there is an added level of complexity here, as the primary quantitative result is manipulated ie, placed into a category.
The categorized results for these tests retain a quantitative nature although this is distinct from the quantitative primary data and, in practice, trueness and precision can be determined at the category level, as well as at the level of the primary result. We divide categorical tests into two subgroups, depending on the number and type of categories and the degree of importance placed on knowing how accurate a result is Figure 4.
Each category indicated by alternating shading has an upper cutoff that is also the lower cutoff of the next category. Each category shaded has unique upper and lower cutoffs. Results falling between categories are classed as unreportable marked with an arrow. A dosage quotient DQ of 0. This group includes tests in which there are essentially unlimited categories, such as the sizing example cited above. In this case, each cutoff forms the upper boundary of one category and the lower boundary of the next, so that all results can be categorized except for those that have failed.
Generally, less-stringent levels of accuracy are acceptable with this type of test. When the number of predefined categories is limited, for example, with allele copy number determination, accuracy tends to be critical and a more definitive approach is often required. The most informative way to express accuracy for this type of test is the probability that a particular quantitative result falls into a particular category. Results can be assigned to the appropriate categories by a process of competitive hypotheses testing. For example, a test to determine constitutional allele copy number has three expected results: normal 2n , deleted n and duplicated 3n.
The odds ratios p 2n :p n and p 2n :p 3n can be used to assign results Figure 5. It should be noted that mosaic variants may give rise to intermediate values; detection of mosaics should be considered under quantitative tests. Multiplex ligation-dependent probe amplification to detect exon copy number Categorical test type C. Results falling between categories are unreportable.
This is the extreme form of a categorical test, in which there are only two result categories, positive and negative. This binary categorization can be based either on a cutoff applied to a quantitative result, for example, peak height or a mathematical measure representing peak shape, or on direct qualitative observation by the analyst, for example, the presence or absence of a peak in the latter case, as discussed in the section 'Types of test', the underlying data will generally be quantitative in nature, even though no formal quantification is performed.
In terms of accuracy, categorization can be either correct or incorrect with respect to the 'true' reference result. A simple contingency table can be used to describe the four possible outcomes Table 3. The diagnostic accuracy of a qualitative test can be characterized by two components, both of which can be calculated from the figures in the contingency table:.
For comparison with quantitative tests Figure 3 , the relationship between the components of accuracy is depicted in Figure 6.
The relationship between performance characteristics, error and measurement uncertainty used for qualitative tests adapted from Menditto et al There is an inverse relationship between sensitivity and specificity Figure 7. As more stringent cutoffs are used to reduce the number of false positives ie, increase specificity , the likelihood of false negatives increases.
Therefore, the desirable characteristics of a test must be considered in the context of the required outcome and the diagnostic consequences. For example, laboratory procedures for mutation scanning tests often involve a primary screen to determine which fragments carry mutations, followed by a second confirmatory test by sequencing to characterize the mutations present. In the primary screen, sensitivity is much more critical than specificity, to avoid missing mutations that are present; the only consequence of poor specificity is increase in the workload for confirmatory sequencing.
Obviously, there is a limit to the lack of specificity that can be tolerated, even if only on the grounds of cost and efficiency. The figure shows frequency distributions of the primary quantitative results for a qualitative binary test. Solid line represents gold standard negatives wild type , broken line represents gold standard positives mutant. Using a single cutoff to categorize the results as either positive or negative gives rise to both false negatives and false positives.
Positioning the cutoff to the right encompasses more of the negative distribution, giving a low false-positive rate but a high false-negative rate shaded. As the cutoff is moved to the left, the false-negative rate is reduced but the false-positive rate increases. It is possible to minimize both false-positive and false-negative rates by using two cutoffs.
In this case, results falling between the two cutoffs can either be classified as test failures or be passed for further analysis.