Setting Acceptance Criteria for Analytical Performance Characteristics

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-27

Quick Answer

• Set acceptance criteria for analytical method validation using the total error approach, which combines bias and imprecision to reflect real-world method performance against a defined allowable error.
• Base criteria on the intended clinical or research use of the assay, not on arbitrary or generic thresholds, and document the scientific justification for each criterion.
• Recognize that acceptance criteria are method-specific and matrix-dependent, so criteria validated for one sample type or instrument may not transfer to another without revalidation.

At a Glance

The following table summarizes the key analytical performance characteristics that require acceptance criteria during method validation, along with the primary considerations for setting each criterion.

The Problem with Arbitrary Acceptance Criteria

Analytical method validation requires a defensible basis for deciding whether a method performs well enough for its intended purpose. Many laboratories default to generic thresholds, such as accepting any coefficient of variation below 10 percent or any recovery between 90 and 110 percent, without linking those numbers to the clinical or research question the assay must answer. These arbitrary thresholds create a false sense of assurance. A method that meets a generic precision target may still produce results that are too variable for a diagnostic application that requires narrow confidence intervals, while a method that fails a generic threshold may be perfectly adequate for a screening application where coarse discrimination is sufficient.

The central problem is that acceptance criteria must be derived from the intended use of the method. The same analytical procedure can be fit for one purpose and unfit for another. A quantitative assay used to monitor a chronic disease marker over time needs tighter precision than a qualitative screening test that only needs to distinguish positive from negative. A method used to support a regulatory submission needs more rigorous validation than a method used for internal research screening. The analyst must therefore define the intended use before selecting acceptance criteria, and the criteria must be documented in the validation protocol.

The total error approach provides a coherent framework for setting acceptance criteria. Total error combines the systematic error (bias) and the random error (imprecision) into a single metric that estimates the maximum deviation of a measured result from the true value. This approach is grounded in measurement science and is used in clinical chemistry and diagnostic laboratory practice. The total error framework is described in the context of laboratory quality programs and measurement science by the National Institute of Standards and Technology, which emphasizes the importance of traceability and measurement uncertainty in laboratory practice. The NIST laboratory programs provide the measurement science foundation for understanding how uncertainty propagates through analytical procedures.

The total error approach requires the analyst to define an allowable total error before validation begins. This allowable error is the maximum acceptable deviation between the measured value and the true value, and it must be justified by the intended use of the method. For a diagnostic assay, the allowable error might be derived from the clinical decision limits that guide patient management. For a research assay, the allowable error might be derived from the biological variation that the assay must detect. Once the allowable total error is defined, the validation data are used to estimate the actual total error, and the method is accepted if the actual error does not exceed the allowable error.

Core Principles of Analytical Performance Validation

Measurement Uncertainty as the Foundation

Measurement uncertainty is the quantitative expression of the doubt that exists about the result of a measurement. Every analytical measurement carries uncertainty from multiple sources, including the calibration of the instrument, the purity of the reagents, the skill of the operator, and the environmental conditions during the analysis. The uncertainty of a measurement is not a defect in the method but a property of the measurement process, and it must be estimated and reported for the result to be interpretable.

The National Institute of Standards and Technology operates laboratory programs that develop and maintain measurement standards, and these programs provide the basis for understanding how measurement uncertainty propagates through analytical procedures [NIST Laboratory Programs]. The NIST approach emphasizes that measurement results are meaningful only when they are accompanied by an estimate of their uncertainty. This principle applies directly to the validation of analytical methods: the acceptance criteria for a method must be expressed in terms of the uncertainty that the method can achieve, and the validation data must demonstrate that the method achieves the required uncertainty.

The uncertainty of an analytical method is composed of two main components. The first component is the systematic error, or bias, which is the difference between the measured value and the true value. The bias can be estimated by analyzing certified reference materials with known concentrations or by comparing the method against a reference method. The second component is the random error, or imprecision, which is the variation that occurs when the same sample is measured repeatedly. The imprecision is estimated by the standard deviation of the repeated measurements.

The total error of the method is the combination of the bias and the imprecision. The total error is calculated as the bias plus a multiple of the standard deviation, where the multiple is chosen to provide the desired confidence level. For a 95 percent confidence level, the total error is the bias plus 1.96 times the standard deviation. This total error represents the maximum expected deviation of a single measured result from the true value, and it is the quantity that must be compared against the allowable error.

The Role of Intended Use

The intended use of the method is the primary determinant of the acceptance criteria. A method that is intended to support a clinical diagnosis must meet criteria that ensure the results are reliable enough to guide patient management. A method that is intended to screen samples for the presence of a target must meet criteria that ensure the results are reliable enough to distinguish positive from negative. A method that is intended to quantify a target for research purposes must meet criteria that ensure the results are reliable enough to support the experimental conclusions.

The intended use determines the allowable total error. For a clinical diagnostic assay, the allowable total error is often derived from the biological variation of the analyte or from the clinical decision limits that guide the interpretation of the results. For a research assay, the allowable total error is often derived from the magnitude of the effect that the study is designed to detect. The analyst must document the rationale for the allowable total error, and the rationale must be based on the intended use of the method.

The intended use also determines the validation design. A method that is intended for quantitative measurement requires a validation design that includes the estimation of bias and imprecision across the measuring range. A method that is intended for qualitative detection requires a validation design that includes the estimation of the limit of detection and the specificity of the method. The validation design must be documented in the validation protocol, and the acceptance criteria must be defined before the validation data are collected.

Regulatory Guidance and the Total Error Approach

The total error approach is consistent with the regulatory expectations for analytical method validation in the clinical laboratory and pharmaceutical industries. The approach is described in the context of the quality control and quality assurance programs that are required for clinical laboratories. The National Library of Medicine provides access to the molecular biology and laboratory methods references that describe the standard validation procedures [National Library of Medicine books]. These references describe the standard procedures for evaluating the performance of analytical methods, including the estimation of precision, accuracy, and the limit of detection.

The regulatory guidance for analytical method validation emphasizes that the acceptance criteria must be based on the intended use of the method and that the validation data must demonstrate that the method meets the criteria. The guidance also emphasizes that the validation must be documented, and that the documentation must include the protocol, the data, and the conclusions. The total error approach provides a framework for meeting these expectations because it requires the analyst to define the allowable error, to estimate the actual error, and to document the comparison.

The total error approach is particularly useful for methods that are used to support clinical decisions because it provides a direct estimate of the risk that a single result will be misleading. The total error is the maximum expected deviation of a single result from the true value, and this deviation is the quantity that matters for the clinical interpretation. A method with a total error that is smaller than the allowable error is acceptable, and a method with a total error that exceeds the allowable error is not acceptable.

Setting Acceptance Criteria for Precision

Precision is the degree of agreement between repeated measurements of the same sample. It is expressed as the standard deviation or the coefficient of variation of the repeated measurements. Precision is a critical performance characteristic because it determines the reliability of a single measurement. A method with poor precision produces results that vary widely between repeated measurements, and this variation can obscure the true value of the sample.

The acceptance criteria for precision must be based on the intended use of the method. For a method that is used to monitor a patient over time, the precision must be tight enough to detect a clinically significant change in the analyte concentration. For a method that is used to screen samples for the presence of a target, the precision must be tight enough to avoid false positive and false negative results. The analyst must define the acceptable precision in terms of the standard deviation or the coefficient of variation, and the criteria must be documented in the validation protocol.

The precision of a method is estimated by measuring the same sample multiple times and calculating the standard deviation of the results. The precision is typically estimated at multiple concentration levels across the measuring range, because the precision often varies with the concentration. The precision at the lower end of the range is often worse than the precision at the higher end of the range, and the acceptance criteria must account for this variation.

The precision of a method is also affected by the conditions of the measurement. The within-run precision is the variation that occurs when the same sample is measured multiple times in the same run. The between-run precision is the variation that occurs when the same sample is measured in different runs, and the between-operator precision is the variation that occurs when different operators perform the measurement. The acceptance criteria must be defined for each of these precision components, and the validation must include the data to estimate each component.

Within-Run and Between-Run Precision

The within-run precision is the variation that occurs when the same sample is measured multiple times in the same analytical run. This precision component reflects the short-term variability of the method, and it is typically the smallest of the precision components. The within-run precision is estimated by measuring the same sample multiple times in a single run and calculating the standard deviation of the results.

The between-run precision is the variation that occurs when the same sample is measured in different runs. This precision component reflects the long-term variability of the method, including the variation that occurs between different calibrations, different reagent lots, and different operators. The between-run precision is estimated by measuring the same sample in multiple runs and calculating the standard deviation of the results.

The acceptance criteria for the within-run and between-run precision must be defined separately because the two components have different implications for the clinical interpretation. The within-run precision affects the reliability of a single measurement, while the between-run precision affects the reliability of the comparison of results from different runs. The between-run precision is typically the more important component for clinical monitoring, because the clinical interpretation often involves the comparison of results from different runs.

Precision and the Coefficient of Variation

The coefficient of variation is the standard deviation expressed as a percentage of the mean. It is a useful measure of precision because it allows the comparison of precision across different concentration levels. The coefficient of variation is calculated as the standard deviation divided by the mean, multiplied by 100.

The acceptance criteria for precision are often expressed in terms of the coefficient of variation. The analyst must define the maximum acceptable coefficient of variation for each concentration level, and the validation data must demonstrate that the method achieves the required coefficient of variation. The coefficient of variation is typically higher at the lower concentration levels, and the acceptance criteria must account for this variation.

The coefficient of variation is a useful metric for comparing the precision of different methods, but it has limitations. The coefficient of variation is not meaningful when the mean is close to zero, because the coefficient of variation becomes very large. The coefficient of variation is also not appropriate for methods that produce results on a nonlinear scale. The analyst must consider these limitations when defining the acceptance criteria.

Setting Acceptance Criteria for Accuracy

The accuracy of a method is the closeness of the measured value to the true value. The accuracy is the combination of the bias and the imprecision of the method. The bias is the systematic error of the method, which is the difference between the measured value and the true value. The imprecision is the random error of the method, which is the variation between repeated measurements.

The acceptance criteria for accuracy must be defined in terms of the total error, which is the combination of the bias and the imprecision. The total error is the maximum expected deviation of a single measured result from the true value, and it is the quantity that must be compared against the allowable error. The total error is calculated as the bias plus 1.96 times the standard deviation, which provides a 95 percent confidence level.

The bias of the method is estimated by measuring a certified reference material with a known concentration and calculating the difference between the measured value and the certified value. The bias can also be estimated by comparing the method against a reference method that has a documented uncertainty. The bias is expressed as a percentage of the true value, and the acceptance criteria for the bias must be defined in the validation protocol.

The accuracy of the method is also affected by the recovery of the method, which is the proportion of the analyte that is measured relative to the amount that is present in the sample. The recovery is estimated by spiking a sample with a known amount of the analyte and measuring the amount that is recovered. The recovery is expressed as a percentage, and the acceptance criteria for the recovery must be defined in the validation protocol.

Using Certified Reference Materials

Certified reference materials are materials with a known concentration of the analyte, and they are used to estimate the bias of the method. The certified reference material is measured multiple times, and the mean of the measurements is compared to the certified value. The difference between the mean and the certified value is the bias of the method.

The certified reference material must be appropriate for the method. The matrix of the certified reference material must be similar to the matrix of the samples that the method will measure, and the concentration of the certified reference material must be within the measuring range of the method. The certified reference material must also be traceable to a reference standard, and the uncertainty of the certified value must be documented.

The National Institute of Standards and Technology provides the reference materials and the measurement standards that are used to establish the traceability of analytical methods [NIST laboratory programs]. The NIST reference materials are used to calibrate the instruments and to verify the accuracy of the methods. The use of certified reference materials is a critical component of the validation of the accuracy of the method.

Recovery Studies and Matrix Effects

The recovery of the method is the percentage of the analyte that is measured relative to the amount that is present in the sample. The recovery is estimated by spiking a sample with a known amount of the analyte and measuring the concentration of the analyte in the spiked sample. The recovery is calculated as the measured concentration divided by the expected concentration, multiplied by 100.

The recovery of the method is affected by the matrix of the sample. The matrix is the composition of the sample, including the proteins, the lipids, and the other components that are present. The matrix can interfere with the measurement of the analyte, and the recovery can be lower than 100 percent if the matrix interferes with the measurement. The recovery can also be higher than 100 percent if the matrix enhances the measurement.

The acceptance criteria for the recovery must be defined based on the intended use of the method. For a method that is used to quantify the analyte in a complex matrix, the recovery must be close to 100 percent, and the acceptance criteria must be defined accordingly. For a method that is used to screen the samples for the presence of the analyte, the recovery may be less critical, and the acceptance criteria may be more lenient.

Setting Acceptance Criteria for Analytical Sensitivity

The analytical sensitivity of a method is the ability of the method to detect a small amount of the analyte. The analytical sensitivity is expressed in terms of the limit of detection and the limit of quantification. The limit of detection is the lowest concentration of the analyte that can be detected with a specified level of confidence. The limit of quantification is the lowest concentration of the analyte that can be quantified with a specified level of precision and accuracy.

The limit of detection is estimated by measuring a blank sample multiple times and calculating the standard deviation of the blank measurements. The limit of detection is the mean of the blank measurements plus a multiple of the standard deviation, where the multiple is chosen based on the desired confidence level. The limit of detection is typically defined as the mean of the blank plus 3 times the standard deviation, which provides a 99.7 percent confidence level.

The limit of quantification is estimated by measuring a sample with a low concentration of the analyte multiple times and calculating the standard deviation of the measurements. The limit of quantification is the lowest concentration that can be measured with a specified level of precision and accuracy. The limit of quantification is typically defined as the lowest concentration that can be measured with a coefficient of variation of 20 percent or less.

The acceptance criteria for the limit of detection and the limit of quantification must be defined based on the intended use of the method. For a method that is used to detect the presence of a target, the limit of detection must be low enough to detect the target at the concentration that is clinically relevant. For a method that is used to quantify the target, the limit of quantification must be low enough to quantify the target at the concentration that is clinically relevant.

Limit of Blank, Limit of Detection, and Limit of Quantification

The limit of blank is the highest concentration of the analyte that is expected to be found in a blank sample. The limit of blank is estimated by measuring a blank sample multiple times and calculating the mean and the standard deviation of the blank measurements. The limit of blank is the mean of the blank measurements plus 1.645 times the standard deviation, which provides a 95 percent confidence level.

The limit of detection is the lowest concentration of the analyte that can be detected from the limit of blank. The limit of detection is estimated by measuring a sample with a low concentration of the analyte multiple times and calculating the standard deviation of the measurements. The limit of detection is the limit of blank plus 1.645 times the standard deviation of the low concentration sample.

The limit of quantification is the lowest concentration of the analyte that can be quantified with a specified level of precision and accuracy. The limit of quantification is estimated by measuring a sample with a low concentration of the analyte multiple times and calculating the coefficient of variation of the measurements. The limit of quantification is the lowest concentration that can be measured with a coefficient of variation of 20 percent or less.

The acceptance criteria for the limit of blank, the limit of detection, and the limit of quantification must be defined based on the intended use of the method. The limit of blank must be low enough to avoid false positive results, and the limit of detection must be low enough to detect the target at the clinically relevant concentration. The limit of quantification must be low enough to quantify the target at the clinically relevant concentration.

Analytical Sensitivity and the Measuring Range

The measuring range of the method is the range of concentrations over which the method produces valid results. The measuring range is defined by the lower limit of quantification and the upper limit of quantification. The lower limit of quantification is the lowest concentration that can be quantified with the specified precision and accuracy, and the upper limit of quantification is the highest concentration that can be quantified with the specified precision and accuracy.

The measuring range is established by measuring samples with known concentrations across the range and verifying that the method produces accurate and precise results. The measuring range is typically established by measuring a series of samples with increasing concentrations and plotting the measured concentration against the known concentration. The measuring range is the range of concentrations where the measured concentration is linearly related to the known concentration.

The acceptance criteria for the measuring range must be defined based on the intended use of the method. The measuring range must include the concentrations that are clinically relevant, and the method must produce accurate and precise results across the entire range. The measuring range must be documented in the validation protocol, and the validation data must demonstrate that the method produces valid results across the entire range.

Setting Acceptance Criteria for Analytical Specificity

The analytical specificity of a method is the ability of the method to measure only the analyte of interest without interference from other components. The specificity of the method is evaluated by testing the method with samples that contain the potential interfering substances. The interfering substances include the structurally related compounds, the metabolites, and the other components that are present in the sample matrix.

The acceptance criteria for the specificity must be defined based on the intended use of the method. For a method that is used to measure a specific analyte in a complex matrix, the specificity must be high enough to avoid interference from the other components. For a method that is used to screen a sample for the presence of a target, the specificity must be high enough to avoid false positive results.

The specificity of the method is evaluated by measuring the samples that contain the potential interfering substances and comparing the results to the results of the samples that do not contain the interfering substances. The interference is the difference between the measured concentration in the presence of the interfering substance and the measured concentration in the absence of the interfering substance. The acceptance criteria for the interference must be defined based on the intended use of the method.

Interference Testing and Cross-Reactivity

The interference testing is the process of evaluating the effect of the potential interfering substances on the measurement of the analyte. The interfering substances include the compounds that are structurally similar to the analyte, the metabolites of the analyte, and the other components that are present in the sample matrix. The interference testing is performed by spiking the sample with the potential interfering substance and measuring the concentration of the analyte.

The cross-reactivity is the ability of the method to detect the compounds that are structurally similar to the analyte. The cross-reactivity is evaluated by testing the samples that contain the structurally similar compounds and measuring the response of the method. The cross-reactivity is the percentage of the response that is produced by the structurally similar compound relative to the response that is produced by the analyte.

The acceptance criteria for the interference and the cross-reactivity must be defined based on the intended use of the method. For a method that is used to measure a specific analyte, the interference and the cross-reactivity must be low enough to avoid the false results. For a method that is used to screen a sample for the presence of a target, the interference and the cross-reactivity must be low enough to avoid the false positive results.

The Total Error Approach in Practice

The total error approach provides a practical framework for setting the acceptance criteria for the analytical method validation. The approach requires the analyst to define the allowable total error, to estimate the actual total error, and to compare the actual total error to the allowable total error. The total error is the combination of the bias and the imprecision, and it is the maximum expected deviation of a single measured result from the true value.

The total error is calculated as the bias plus 1.96 times the standard deviation. The bias is the systematic error of the method, and the standard deviation is the random error of the method. The total error is expressed as a percentage of the true value, and the acceptance criteria are defined in terms of the total error.

The total error approach is consistent with the regulatory expectations for the clinical laboratory validation. The approach is described in the context of the clinical laboratory quality programs, and it is used to evaluate the performance of the analytical methods. The total error approach provides a direct estimate of the risk that a single result will be wrong, and it is the basis for the acceptance criteria for the analytical method validation.

Calculating the Total Error

The total error is calculated from the bias and the standard deviation of the method. The bias is the difference between the measured value and the true value, and the standard deviation is the variation between the repeated measurements. The total error is calculated as the bias plus 1.96 times the standard deviation, which provides a 95 percent confidence level.

The total error is expressed as a percentage of the true value. The total error is calculated as the bias percentage plus 1.96 times the coefficient of variation. The bias percentage is the bias divided by the true value, multiplied by 100. The coefficient of variation is the standard deviation divided by the mean, multiplied by 100.

The total error is compared to the allowable total error, which is defined based on the intended use of the method. The method is accepted if the total error is less than or equal to the allowable total error. The method is rejected if the total error is greater than the allowable total error.

Defining the Allowable Total Error

The allowable total error is the maximum acceptable deviation of a single measured result from the true value. The allowable total error is defined based on the intended use of the method, and it is the basis for the acceptance criteria. The allowable total error is derived from the clinical decision limits, the biological variation, or the experimental requirements.

For a clinical method, the allowable total error is often derived from the clinical decision limits. The clinical decision limits are the concentrations that are used to guide the clinical interpretation, and the allowable total error is the maximum deviation that does not change the clinical interpretation. The allowable total error is the difference between the clinical decision limit and the concentration that would change the clinical interpretation.

For a research method, the allowable total error is often derived from the magnitude of the effect that the study is designed to detect. The allowable total error is the maximum deviation that does not obscure the effect of the study. The allowable total error is the difference between the effect size and the deviation that would obscure the effect.

Practical Steps for Setting Acceptance Criteria

The following steps provide a practical framework for setting the acceptance criteria for the analytical method validation. The steps are based on the total error approach, and they are designed to be used by the analysts who are responsible for the validation of the analytical methods.

Step 1: Define the Intended Use

The first step is to define the intended use of the method. The intended use includes the clinical or research question that the method is designed to answer, the sample type that the method will measure, and the concentration range that the method will cover. The intended use is the basis for the acceptance criteria, and it must be documented in the validation protocol.

The intended use is defined by the clinical or research question. For a clinical method, the intended use is the clinical decision that the method will support. For a research method, the intended use is the experimental question that the method will answer. The intended use must be specific enough to define the acceptance criteria.

Step 2: Define the Allowable Total Error

The second step is to define the allowable total error. The allowable total error is the maximum acceptable deviation of a single measured result from the true value. The allowable total error is derived from the intended use of the method, and it is the basis for the acceptance criteria.

The allowable total error is defined based on the clinical or research requirements. For a clinical method, the allowable total error is derived from the clinical decision limits. For a research method, the allowable total error is derived from the experimental requirements. The allowable total error must be documented in the validation protocol.

Step 3: Design the Validation Study

The third step is to design the validation study. The validation study must include the experiments that are needed to estimate the bias, the imprecision, the limit of detection, the limit of quantification, and the specificity of the method. The validation study must be designed to provide the data that are needed to compare the actual performance to the acceptance criteria.

The validation study must include the samples that are needed to estimate the performance of the method. The samples include the certified reference materials, the spiked samples, and the patient samples. The validation study must include the number of replicates that are needed to estimate the performance with the required confidence.

Step 4: Collect the Validation Data

The fourth step is to collect the validation data. The validation data must be collected according to the validation protocol, and the data must be recorded in the validation report. The validation data must include the results of the measurements, the conditions of the measurements, and the calculations of the performance.

The validation data must be collected under the conditions that are representative of the routine use of the method. The validation data must include the measurements of the certified reference materials, the spiked samples, and the patient samples. The validation data must be collected over multiple runs and by multiple operators to estimate the between-run and between-operator precision.

Step 5: Compare the Performance to the Acceptance Criteria

The fifth step is to compare the performance of the method to the acceptance criteria. The performance of the method is the estimated bias, the precision, the limit of detection, the limit of quantification, and the range. The performance is compared to the acceptance criteria, and the method is accepted if the performance meets the criteria.

The comparison of the performance to the acceptance criteria must be documented in the validation report. The validation report must include the performance data, the acceptance criteria, and the conclusion of the comparison. The validation report must be reviewed and approved by the responsible personnel.

Records and Measurements

The validation of the analytical method requires the documentation of the validation protocol, the validation data, and the validation report. The validation protocol describes the intended use of the method, the acceptance criteria, and the validation design. The validation data include the results of the validation experiments. The validation report includes the comparison of the performance to the acceptance criteria and the conclusion of the validation.

The validation protocol must be written before the validation experiments are performed. The protocol must include the intended use of the method, the acceptance criteria, and the validation design. The protocol must be reviewed and approved by the responsible personnel before the validation experiments are performed.

The validation data must be recorded in the laboratory notebook or the electronic laboratory record. The data must include the results of the measurements, the conditions of the measurements, and the calculations of the performance. The data must be recorded in a way that is traceable and that allows the validation to be repeated.

The validation report must include the comparison of the performance to the acceptance criteria and the conclusion of the validation. The report must include the performance data, the acceptance criteria, and the conclusion of the comparison. The report must be reviewed and approved by the responsible personnel.

Common Failure Patterns

The following are the common failure patterns that are observed during the validation of the analytical methods. The failure patterns are the result of the errors in the validation design, the validation execution, or the validation interpretation.

Failure to Define the Intended Use

The most common failure pattern is the failure to define the intended use of the method. The acceptance criteria are defined without a clear understanding of the clinical or research question that the method is designed to answer. The result is that the acceptance criteria are arbitrary and do not reflect the requirements of the method.

The failure to define the intended use is often the result of the lack of communication between the analysts and the clinical or research personnel. The analysts must work with the clinical or research personnel to define the intended use of the method, and the acceptance criteria must be based on the intended use.

Failure to Use the Total Error Approach

The second common failure pattern is the failure to use the total error approach. The acceptance criteria are defined in terms of the bias and the precision separately, without considering the combined effect of the bias and the precision. The result is that the method may meet the individual criteria but fail to meet the total error requirement.

The total error approach is the basis for the acceptance criteria, and the acceptance criteria must be defined in terms of the total error. The total error is the combination of the bias and the precision, and the acceptance criteria must be defined in terms of the total error.

Failure to Document the Validation

The third common failure pattern is the failure to document the validation. The validation protocol, the validation data, and the validation report are not documented, and the validation cannot be repeated or reviewed. The result is that the validation is not defensible, and the method cannot be used for the intended purpose.

The validation must be documented in the validation protocol, the validation data, and the validation report. The documentation must be complete and accurate, and the documentation must be reviewed and approved by the responsible personnel.

Quality Control and Quality Assurance

The quality control and the quality assurance are the processes that are used to ensure that the analytical method continues to perform at the level that was established during the validation. The quality control is the process of monitoring the performance of the method during the routine use. The quality assurance is the process of ensuring that the method is performing at the intended level.

The quality control is the process of measuring the control samples during the routine use of the method. The control samples are the samples with the known concentrations of the analyte, and they are measured at the beginning and the end of each run. The results of the control samples are compared to the acceptance criteria, and the run is accepted if the control results are within the acceptance criteria.

The quality assurance is the process of ensuring that the method is performing at the intended level. The quality assurance includes the review of the quality control data, the review of the calibration data, and the review of the maintenance records. The quality assurance is the process of ensuring that the method is performing at the intended level.

The National Institute of Standards and Technology provides the measurement standards and the quality programs that are used to ensure the quality of the analytical results. The NIST laboratory programs provide the framework for the measurement science and the quality assurance [NIST laboratory programs]. The quality of the analytical results is based on the traceability of the results to the reference standards and the quality of the measurement process.

Biosafety and Regulatory Context

The validation of the analytical methods must be conducted in accordance with the biosafety and the regulatory requirements. The biosafety requirements are the requirements for the safe handling of the biological samples and the reagents. The regulatory requirements are the requirements for the validation of the analytical methods.

The biosafety requirements are described in the Biosafety in Microbiological and Biomedical Laboratories, which is the official guidance for the biosafety in the laboratory [CDC and NIH biosafety guidance]. The guidance describes the principles of the biosafety, the risk assessment, and the containment of the biological agents. The validation of the analytical methods must be conducted in accordance with the biosafety requirements.

The regulatory requirements for the validation of the analytical methods are described in the guidance for the clinical laboratory and the diagnostic methods. The regulatory requirements include the requirements for the validation of the analytical methods, the documentation of the validation, and the quality of the analytical results. The validation of the analytical methods must be conducted in accordance with the regulatory requirements.

The National Institutes of Health provides the guidelines for the research involving the recombinant or synthetic nucleic acid molecules [NIH guidelines for recombinant and synthetic nucleic acid molecules]. The guidelines describe the requirements for the research involving the recombinant and synthetic nucleic acid molecules, and the validation of the analytical methods must be conducted in accordance with the guidelines.

Frequently Asked Questions

What is the total error approach in analytical method validation?

The total error approach combines the bias and the imprecision of a method into a single measure that estimates the maximum expected deviation of a single result from the true value. The total error is calculated as the bias plus 1.96 times the standard deviation, and it is compared to the allowable total error that is defined based on the intended use of the method.

How do I define the allowable total error for my method?

The allowable total error is defined based on the intended use of the method. For a clinical method, the allowable total error is derived from the clinical decision limits. For a research method, the allowable total error is derived from the experimental requirements. The allowable total error must be documented in the validation protocol.

What is the difference between the limit of detection and the limit of quantification?

The limit of detection is the lowest concentration of the analyte that can be detected from the limit of blank. The limit of quantification is the lowest concentration of the analyte that can be quantified with a specified level of precision and accuracy. The limit of quantification is typically higher than the limit of detection.

How do I estimate the bias of an analytical method?

The bias of the method is estimated by measuring a certified reference material with a known concentration and calculating the difference between the measured value and the known value. The bias can also be estimated by comparing the method against a reference method with a documented uncertainty.

What is the coefficient of variation and how is it used?

The coefficient of variation is the standard deviation expressed as a percentage of the mean. It is used to express the precision of the method in relative terms, and it allows the comparison of the precision across different concentration levels. The coefficient of variation is calculated as the standard deviation divided by the mean, multiplied by 100.

What is the role of certified reference materials in validation?

Certified reference materials are used to estimate the bias of the method. The certified reference material has a known concentration of the analyte, and the bias is the difference between the measured value and the known value. The certified reference material must be traceable to a reference standard, and the uncertainty of the certified value must be documented.

How do I evaluate the specificity of an analytical method?

The specificity of the method is evaluated by testing the samples that contain the potential interfering substances. The interference is the difference between the measured concentration in the presence of the interfering substance and the measured concentration in the absence of the interfering substance. The acceptance criteria for the interference must be defined based on the intended use of the method.

What documentation is required for the validation of an analytical method?

The validation must be documented in the validation protocol, the validation data, and the validation report. The validation protocol must include the intended use of the method, the acceptance criteria, and the validation design. The validation data must include the results of the validation experiments. The validation report must include the comparison of the performance to the acceptance criteria and the conclusion of the validation.

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• Quality Indicators in Laboratory Medicine: Key Metrics for Performance Monitoring
• Cell Lysis Methods: Choosing the Right Approach for Your Sample
• Automated NGS Library Preparation: Platforms, Benefits, and Selection Criteria
• Cloud-Based Diagnostic Data Integration for Herd Health Management

References and Further Reading

• NIST Laboratory Programs. National Institute of Standards and Technology.

This article is educational and does not replace validated laboratory procedures, institutional biosafety review, manufacturer instructions, or professional interpretation.