Equivalence Criteria for Analytical Method Transfer

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

Quick Answer

• Equivalence criteria for analytical method transfer compare results between laboratories using predefined acceptance limits based on method variability and risk.
• Select difference testing when methods are expected to match closely, or equivalence testing when you need to prove the new laboratory performs within an acceptable range.
• Acceptance limits must be justified from validation data, not chosen arbitrarily, and documented before the transfer study begins.

At a Glance

Understanding Method Transfer in Analytical Laboratories

Method transfer is the documented process of demonstrating that an analytical procedure performs equivalently in a receiving laboratory compared to the originating laboratory. This process is essential when a diagnostic assay, molecular biology test, or analytical chemistry method moves from a development laboratory to a clinical laboratory, from a research group to a quality control facility, or from one site to another within a regulated organization.

The transfer process requires more than running the same protocol on the same instrument. Each laboratory has its own operators, environmental conditions, reagent lots, and calibration practices. These variables introduce differences that must be quantified and evaluated against pre-defined criteria. Without a statistical framework for comparison, the receiving laboratory cannot determine whether observed differences reflect acceptable variation or a fundamental problem with the method as implemented.

The National Institute of Standards and Technology maintains laboratory programs that emphasize measurement science, calibration, and quality assurance across analytical disciplines. These programs underscore the principle that reliable measurements depend on traceable standards and documented procedures. For method transfer, this means the acceptance criteria must be grounded in the known performance characteristics of the method, not in an arbitrary tolerance chosen after the data are collected.

Core Principles of Equivalence Criteria

The Distinction Between Difference Testing and Equivalence Testing

A common error in method transfer is using a traditional hypothesis test to compare results from two laboratories. A t-test or ANOVA asks whether the observed difference between means is statistically significant. When the test returns a non-significant result, many teams conclude that the methods are equivalent. This conclusion is statistically invalid. A non-significant result can occur because the methods are truly similar, but it can also occur because the sample size is too small to detect a real difference.

Equivalence testing reverses the logic. Instead of testing whether the methods differ, equivalence testing asks whether the difference between the methods falls within a pre-defined acceptable range. The two one-sided tests (TOST) procedure is the most common approach. This procedure constructs a confidence interval for the difference between the two laboratories and compares that interval to the equivalence bounds. If the entire confidence interval falls within the bounds, the methods are declared equivalent.

The choice between difference testing and equivalence testing changes the entire design of the transfer study. Difference testing requires a sample size calculation based on the expected difference and the desired power to detect that difference. Equivalence testing requires a sample size calculation based on the equivalence limits and the expected variability of the method.

Selecting Equivalence Limits

The equivalence limits define the maximum acceptable difference between the two laboratories. These limits are the most consequential decision in the transfer study because they determine whether the transfer passes or fails. The limits must be based on the method's known variability and the clinical or analytical requirements of the test.

Using Validation Data to Set Limits

The original laboratory should have validation data that describe the method's precision, accuracy, and reproducibility. These data provide the foundation for setting equivalence limits. A common approach is to express the limits as a percentage of the mean result or as a multiple of the standard deviation observed during validation.

For example, if a quantitative assay has a within-laboratory coefficient of variation of 5 percent during validation, the transfer team might set the equivalence limits at 10 percent of the mean. This limit allows for the additional variability that comes from a different laboratory while still ensuring that the results are clinically meaningful.

The limits should be documented in the transfer protocol before any data are collected. This pre-specification prevents the team from adjusting the limits after seeing the results, which would compromise the statistical validity of the study.

Risk-Based Limit Selection

The acceptable difference between laboratories depends on how the results will be used. A diagnostic assay that determines a treatment decision requires tighter limits than a research assay that measures a relative change in gene expression. The transfer team should consider the consequences of a false acceptance or a false rejection.

A false acceptance occurs when the transfer study passes but the receiving laboratory produces results that differ from the original laboratory in a way that affects clinical decisions. A false rejection occurs when the transfer study fails but the receiving laboratory actually performs acceptably. Both errors have costs, and the limits should be chosen to balance these costs.

The MIQE guidelines for quantitative PCR emphasize the importance of transparency in reporting and the need for controls that allow the reader to assess the quality of the data. The same principle applies to method transfer. The acceptance limits and the statistical approach should be documented so that the transfer study can be evaluated by others.

Statistical Approaches for Equivalence Assessment

Two One-Sided Tests (TOST)

The TOST procedure is the most widely used approach for equivalence testing. The procedure tests two hypotheses. The first hypothesis is that the difference between the methods is greater than the upper equivalence limit. The second hypothesis is that the difference is less than the lower equivalence limit. If both hypotheses are rejected, the methods are declared equivalent.

The TOST procedure is equivalent to constructing a confidence interval for the difference and checking whether that interval falls entirely within the equivalence bounds. The confidence level for the interval is typically 90 percent when the overall significance level is 5 percent, because the two one-sided tests each use a significance level of 5 percent.

The TOST procedure requires a sample size that is large enough to produce a confidence interval that is narrower than the equivalence range. The sample size depends on the variability of the method and the width of the equivalence range. A method with high variability requires more samples to achieve the same confidence interval width.

Regression-Based Approaches

An alternative approach uses linear regression to compare the results from the two laboratories. The receiving laboratory results are regressed on the original laboratory results. The slope and intercept of the regression line are then compared to the ideal values of 1 and 0. If the confidence intervals for the slope and intercept include these ideal values, the methods are considered equivalent.

This approach has the advantage of detecting proportional and constant differences separately. A slope that differs from 1 indicates a proportional bias, where the difference between the laboratories increases with the concentration of the analyte. An intercept that differs from 0 indicates a constant bias, where the difference is the same across the entire range.

The regression approach requires samples that span the entire analytical range of the method. If the samples are concentrated in a narrow range, the regression estimates will be unstable and the confidence intervals will be wide.

Total Error and Tolerance Intervals

A third approach compares the total error of the receiving laboratory to the total error of the original laboratory. Total error is the combination of random error and systematic error. The receiving laboratory is considered equivalent if its total error falls within the acceptance limits.

Tolerance intervals are a related concept. A tolerance interval contains a specified proportion of the population with a specified confidence. For method transfer, the team might require that 95 percent of the results from the receiving laboratory fall within a tolerance interval that is based on the original laboratory's results.

This approach is useful when the transfer study includes a large number of samples and the team wants to make a statement about individual results instead of the mean. The tolerance approach is more conservative than the TOST approach because it requires the distribution of results to be similar, beyond the means.

Designing the Transfer Study

Sample Selection

The samples used in the transfer study must represent the range of concentrations or values that the method will encounter in routine use. The samples should include low, medium, and high values, and they should be distributed across the analytical range.

The samples should be identical for both laboratories. The same sample set should be shipped to both laboratories, and the samples should be stable during the shipping and storage period. If the samples are not stable, the differences between the laboratories may reflect sample degradation instead of method performance.

The number of samples and the number of replicates per sample should be determined by the statistical approach. The TOST procedure requires a sample size calculation based on the equivalence limits, the expected variability, and the desired power. The team should perform this calculation before the study begins.

Replicates and Runs

Each sample should be analyzed in multiple replicates, and the replicates should be spread across multiple runs. This design allows the team to estimate the between-run variability and the within-run variability separately. The between-run variability is often larger than the within-run variability, and it is important to capture this variability in the transfer study.

The number of replicates per sample should be balanced between the two laboratories. If one laboratory runs more replicates than the other, the confidence interval for the difference will be dominated by the laboratory with fewer replicates.

Blinding and Randomization

The order of sample analysis should be randomized to avoid systematic effects from instrument drift or reagent degradation. The team should also consider blinding the analysts to the sample identity, particularly if the method involves subjective interpretation.

Blinding is less important for fully automated methods, but it is essential for methods that require manual steps or visual interpretation. The team should document the blinding procedure in the transfer protocol.

Practical Implementation Steps

Step 1: Define the Acceptance Criteria

The transfer team should meet before the study begins to define the acceptance criteria. The criteria should include the equivalence limits, the statistical approach, and the sample size. The criteria should be based on the validation data and the clinical or analytical requirements of the method.

The acceptance criteria should be documented in a transfer protocol that is approved by both laboratories. The protocol should also describe the samples, the replicates, the runs, and the data analysis plan.

Step 2: Prepare the Samples

The samples should be prepared in a single laboratory and shipped to both laboratories. The samples should be labeled with a code that does not reveal the concentration or the identity. The samples should be stored under the conditions specified in the method.

The team should also prepare control samples that are known to be stable and that have a known value. These controls should be analyzed in each run to verify that the method is performing correctly.

Step 3: Run the Transfer Study

Each laboratory should follow the method exactly as written. The laboratories should not make any adjustments to the method during the study. If a laboratory encounters a problem, the problem should be documented and the run should be repeated.

The data should be recorded in a format that allows the statistical analysis. The data should include the sample code, the replicate number, the run number, and the result.

Step 4: Analyze the Data

The data should be analyzed using the statistical method that was pre-specified in the protocol. The analysis should be performed by a person who is not involved in the day-to-day running of the study to avoid bias.

The analysis should include a check for outliers. Outliers can have a large effect on the confidence interval, and the team should decide in advance how to handle outliers. The decision should be documented in the protocol.

Step 5: Interpret the Results

The results should be interpreted against the pre-defined acceptance criteria. If the criteria are met, the transfer is successful and the receiving laboratory can begin using the method. If the criteria are not met, the team should investigate the cause of the difference.

The investigation should include a review of the method, the equipment, the reagents, and the operators. The team should also consider whether the acceptance criteria were appropriate for the method.

Records and Measurements

The transfer study should produce a complete record of the data and the analysis. The record should include the following:

• The transfer protocol with the pre-defined acceptance criteria
• The sample preparation and shipping records
• The raw data from both laboratories
• The statistical analysis output
• The interpretation of the results
• The final decision and any follow-up actions

The record should be stored in a location that is accessible to both laboratories. The record should be retained for the period required by the organization's quality system.

The NIST laboratory programs emphasize the importance of measurement assurance and quality control. The transfer record is a key component of the measurement assurance system because it documents the comparability of results between laboratories.

Common Failure Patterns

Failure Pattern 1: Using Difference Testing for Equivalence

The most common failure is to use a t-test or ANOVA to compare the two laboratories and to conclude that the methods are equivalent because the difference is not significant. This conclusion is invalid because the non-significant result may be due to a small sample size.

The correct approach is to use an equivalence test that pre-specifies the acceptable range and tests whether the confidence interval falls within that range.

Failure Pattern 2: Choosing Limits After the Data Are Collected

The team collects the data and then chooses the acceptance limits based on the results. This approach is biased because the limits are chosen to make the transfer pass. The limits must be pre-specified in the protocol before the data are collected.

Failure Pattern 3: Ignoring Between-Run Variability

The team runs all the replicates in a single run and does not capture the between-run variability. The between-run variability is often larger than the within-run variability, and ignoring it can lead to a transfer that passes in the study but fails in practice.

Failure Pattern 4: Using a Narrow Range of Samples

The team uses samples that are concentrated in a narrow range of concentrations. The results may show equivalence in this range, but the method may not be equivalent across the full analytical range.

Failure Pattern 5: Not Documenting the Protocol

The team does not document the acceptance criteria before the study begins. The lack of documentation makes it difficult to evaluate the transfer and can lead to disputes between the laboratories.

Biosafety and Regulatory Context

Method transfer often involves biological samples and reagents that require biosafety precautions. The CDC and NIH provide biosafety guidelines that describe the principles of risk assessment, containment, and institutional oversight. The transfer team should review these guidelines and ensure that both laboratories follow the appropriate biosafety practices.

The NIH Guidelines for Recombinant or Synthetic Nucleic Acid Molecules provide oversight for research involving these materials. If the method involves recombinant or synthetic nucleic acids, the transfer team should ensure that both laboratories comply with the applicable guidelines.

The biosafety considerations should be documented in the transfer protocol. The team should also consider the shipping requirements for biological materials. The samples must be shipped in a way that maintains their stability and prevents exposure to the environment.

Professional Escalation Criteria

The transfer team should escalate the issue to a higher level of authority when the following conditions are met:

• The transfer study fails the acceptance criteria and the cause of the failure cannot be identified
• The acceptance criteria are challenged by one of the laboratories and the team cannot reach an agreement
• The method is used for a clinical decision and the transfer failure could affect patient care
• The transfer involves a new method that has not been validated in the receiving laboratory

The escalation should include a written summary of the issue, the data, and the proposed next steps. The summary should be provided to the appropriate authority, such as the laboratory director or the quality assurance manager.

Decision Framework for Selecting Equivalence Criteria

The statistical approaches described in the previous sections each have distinct strengths, but the transfer team still faces a practical problem: which approach should be used for a specific method? The choice depends on the method's performance characteristics, the intended use of the results, and the regulatory or quality requirements of the organization. This section provides a structured decision framework that guides the team through the selection process using observable method properties and documented risk considerations.

Step 1: Classify the Method by Output Type

The first decision point is the nature of the measurement result. Methods fall into three broad categories that require different equivalence approaches.

Quantitative methods produce a continuous numerical result. Examples include enzyme-linked immunosorbent assays that measure protein concentration, quantitative PCR that measures nucleic acid copy number, and chromatographic methods that measure analyte peak area. These methods support the full range of statistical approaches including TOST, regression, and tolerance intervals.

Qualitative methods produce a categorical result such as positive or negative, present or absent, or a genotype call. These methods require a different statistical framework based on agreement rates, kappa statistics, or the proportion of concordant results. The equivalence limits for qualitative methods are expressed as the minimum acceptable agreement between laboratories, not as a difference in means.

Semi-quantitative methods produce an ordinal result such as a titer or a score. These methods can be analyzed using the same approaches as quantitative methods if the ordinal scale is treated as a continuous variable, but the team should verify that the distribution of results is approximately normal. If the ordinal scale has few categories, the team should use agreement-based statistics instead.

The classification should be documented in the transfer protocol with a justification. The justification should reference the method's validation data and the intended use of the result.

Step 2: Assess the Method Variability from Existing Data

The next decision point uses the method's known variability to determine which statistical approach is feasible. The team should extract the following values from the validation report or from a preliminary precision study:

• The within-laboratory standard deviation or coefficient of variation
• The between-run standard deviation or coefficient of variation
• The total variability observed during validation
• The range of results observed in the validation samples

The MIQE guidelines emphasize the importance of reporting the variability and the conditions under which it was measured. The same principle applies to method transfer. The team should document the source of the variability estimates and the conditions under which they were obtained.

If the method has a coefficient of variation below 10 percent, the TOST approach is usually feasible with a reasonable sample size. If the coefficient of variation is between 10 and 20 percent, the team should consider a larger sample size or a wider equivalence limit. If the coefficient of variation exceeds 20 percent, the team should consider whether the method is suitable for transfer at all, or whether the equivalence limits must be expressed as a multiple of the standard deviation instead of a percentage of the mean.

The team should also assess whether the variability is constant across the analytical range. If the variability increases with the concentration, the team should use a regression approach or a weighted analysis. If the variability is constant, the TOST approach with a single equivalence limit is appropriate.

Step 3: Determine the Risk Level of the Result

The risk level of the method determines how conservative the equivalence criteria must be. The team should classify the method into one of three risk categories.

High-risk methods produce results that directly determine a clinical decision, such as a treatment choice, a diagnosis, or a patient management decision. Examples include diagnostic assays for infectious diseases, therapeutic drug monitoring, and tumor marker quantification. For high-risk methods, the equivalence limits should be tight, the statistical power should be high, and the team should consider using the tolerance interval approach to make a statement about individual results.

Medium-risk methods produce results that are used for monitoring or for research decisions that do not directly affect patient care. Examples include research assays, screening tests, and methods used for lot release testing. For medium-risk methods, the TOST approach with a confidence interval is appropriate.

Low-risk methods produce results that are used for internal quality assessment, for educational purposes, or for preliminary screening. For low-risk methods, the team may use a simpler approach such as a difference test with a pre-specified acceptable difference, but the team should still document the criteria before the study begins.

The risk classification should be documented in the transfer protocol. The team should also document the rationale for the classification, including the intended use of the result and the consequences of an incorrect result.

Step 4: Select the Statistical Approach

The decision framework now combines the method type, the variability, and the risk level to select the statistical approach.

For quantitative methods with low variability and high risk, the team should use the TOST approach with tight equivalence limits. The team should also consider the tolerance interval approach if the method is used for individual patient results.

For quantitative methods with moderate variability and medium risk, the team should use the TOST approach with equivalence limits based on the validation data. The team may also use the regression approach if the method has a wide analytical range and the team wants to detect proportional bias.

For quantitative methods with high variability, the team should use the regression approach or the total error approach. The regression approach is preferred when the variability is proportional to the concentration. The total error approach is preferred when the team wants to compare the overall performance of the two laboratories.

For qualitative methods, the team should use an agreement-based approach. The team should pre-specify the minimum acceptable agreement rate and the confidence level. The team should also consider the kappa statistic, which accounts for the agreement that would occur by chance.

For semi-quantitative methods, the team should use the same approach as for quantitative methods if the ordinal scale is treated as continuous. If the ordinal scale has few categories, the team should use the agreement-based approach.

Step 5: Document the Decision and the Justification

The final step in the decision framework is documentation. The team should record the following in the transfer protocol:

The method classification by output type The variability data used for the decision The risk classification and the rationale The selected statistical approach The equivalence limits and the basis for those limits The sample size calculation and the assumptions used

The documentation should be written so that a person who was not involved in the decision can understand the rationale. The documentation should also be written so that the decision can be audited by a quality assurance group or a regulatory body.

The NIST laboratory programs emphasize the importance of measurement assurance and the documentation of measurement decisions. The decision framework is a key component of the measurement assurance system because it ensures that the equivalence criteria are selected based on data and risk, not on convenience or habit.

Implementing the Decision Framework in Practice

The decision framework is most useful when it is applied before the transfer study begins. The team should schedule a meeting to work through the framework and to document the decisions. The meeting should include the laboratory manager, the analyst who will run the method, and a statistician or a person with statistical expertise.

Practical Implementation Steps

The team should follow these steps to implement the decision framework:

Step 1: Gather the method validation data. The team should collect the validation report, the precision data, and the accuracy data. The team should also collect any data from previous transfer studies that used the same method.

Step 2: Classify the method. The team should classify the method by output type and by risk level. The classification should be based on the method's intended use and the clinical or analytical requirements.

Step 3: Assess the variability. The team should calculate the standard deviation and the coefficient of variation from the validation data. The team should also assess whether the variability is constant across the analytical range.

Step 4: Select the approach. The team should use the decision framework to select the statistical approach. The team should also select the equivalence limits and the sample size.

Step 5: Document the decision. The team should document the decision in the transfer protocol. The documentation should include the rationale for each decision.

Step 6: Review the decision. The team should review the decision after the transfer study is complete. The review should consider whether the decision was appropriate and whether the equivalence criteria were met.

Records and Measurements

The decision framework should produce a record that includes the following:

The method classification by output type The risk classification and the rationale The variability data and the source of the data The selected statistical approach The equivalence limits and the basis for the limits The sample size calculation and the assumptions The date of the decision and the names of the people who made the decision

The record should be stored with the transfer protocol and the transfer report. The record should be retained for the period required by the organization's quality system.

The record should also include the data from the transfer study. The data should include the raw results from both laboratories, the statistical analysis output, and the interpretation of the results. The record should be complete enough that the transfer can be evaluated by a third party.

Common Failure Patterns in Applying the Framework

The decision framework can be applied incorrectly. The team should be aware of the following failure patterns.

Failure Pattern 1: Using the Same Approach for All Methods. The team uses the TOST approach for every method, regardless of the method type or the risk level. This approach is incorrect because qualitative methods require a different statistical framework, and high-risk methods may require a more conservative approach.

Failure Pattern 2: Ignoring the Variability Data. The team selects the equivalence limits without reviewing the validation data. The limits are chosen based on the team's experience with other methods, not on the actual variability of the method being transferred. This approach can lead to limits that are too tight or too wide.

Failure Pattern 3: Choosing the Risk Level Based on the Method Name. The team classifies the risk level based on the method name instead of the intended use. For example, the team may classify a PCR method as low risk because it is used for research, but the method may be used for a clinical decision in a specific context. The risk level should be based on the intended use of the result.

Failure Pattern 4: Not Documenting the Decision. The team makes the decision but does not document the rationale. The lack of documentation makes it difficult to evaluate the transfer and can lead to disputes between the laboratories.

Failure Pattern 5: Not Reviewing the Decision After the Study. The team does not review the decision after the transfer study is complete. The review is important because it allows the team to learn from the transfer and to improve the decision framework for future transfers.

Professional Escalation Criteria

The team should escalate the decision to a higher level of authority when the following conditions are met:

• The team cannot agree on the risk classification of the method
• The variability data are not available or are not sufficient to support the decision
• The selected approach requires a sample size that is not feasible with the available resources
• The equivalence limits are challenged by one of the laboratories and the team cannot reach an agreement
• The method is used for a clinical decision and the transfer failure could affect patient care

The escalation should include a written summary of the decision, the data, and the proposed next steps. The summary should be provided to the appropriate authority, such as the laboratory director or the quality assurance manager.

Comparison of the Decision Framework with Alternative Approaches

The decision framework described in this section provides a structured approach to selecting equivalence criteria. The framework is based on the method type, the variability, and the risk level. The framework is more comprehensive than a simple rule-based approach that uses the same criteria for all methods.

The framework is also more practical than a purely statistical approach that requires the team to perform a complex sample size calculation for every method. The framework allows the team to make a decision based on the available data and the intended use of the method.

The framework is not a substitute for statistical expertise. The team should still consult a statistician for the sample size calculation and the data analysis. The framework provides the structure for the decision, but the statistical implementation requires expertise.

The framework is also not a substitute for the method validation data. The team must have reliable validation data to make the decision. If the validation data are not available, the team should conduct a precision study before the transfer study begins.

The framework is consistent with the principles of the MIQE guidelines, which emphasize the importance of transparency and the documentation of the experimental conditions. The framework also aligns with the measurement science principles of the NIST laboratory programs, which emphasize the importance of traceable standards and documented procedures.

Worked Example of the Decision Framework

The following example illustrates how the decision framework is applied in practice. The example uses a hypothetical quantitative PCR method that measures the expression of a target gene.

Step 1: Classify the method. The method is quantitative because it produces a numerical result. The result is the cycle threshold value, which is converted to a relative expression value.

Step 2: Assess the variability. The validation data show a within-laboratory coefficient of variation of 8 percent and a between-run coefficient of variation of 12 percent. The total coefficient of variation is 15 percent. The variability is constant across the analytical range.

Step 3: Determine the risk. The method is used for a research decision. The result is used to compare the expression of the target gene between two groups of samples. The result is not used for a clinical decision. The risk level is medium.

Step 4: Select the approach. The method is quantitative, the variability is moderate, and the risk is medium. The team selects the TOST approach. The equivalence limits are set at 20 percent of the mean, which is based on the total coefficient of variation of 15 percent plus a margin for the between-laboratory variability.

Step 5: Document the decision. The team documents the decision in the transfer protocol. The protocol includes the method classification, the risk level, the variability data, the equivalence limits, and the sample size calculation.

The team then runs the transfer study. The data are analyzed using the TOST procedure. The confidence interval for the difference between the two laboratories is calculated. The interval falls within the equivalence limits, and the transfer is declared successful.

The team reviews the decision after the study. The team notes that the equivalence limits were appropriate for the method and that the transfer was successful. The team also notes that the decision framework was useful for the decision.

Limitations of the Decision Framework

The decision framework has several limitations that the team should be aware of.

The framework relies on the availability of reliable validation data. If the validation data are not available or are not sufficient, the team cannot make an informed decision. The team should conduct a precision study before the transfer study begins.

The framework does not address the case where the method has a systematic bias. The team should use the regression approach to detect a systematic bias. The framework should be used in conjunction with the regression approach.

The framework does not address the case where the method is used for multiple purposes. The team should classify the risk level based on the highest risk use of the method.

The framework does not address the case where the method is transferred to multiple laboratories. The team should consider the variability between all the laboratories, beyond the two laboratories in the transfer study.

The framework does not address the case where the method is transferred to a laboratory with a different instrument or a different reagent lot. The team should consider the impact of the instrument and the reagent on the method performance.

The framework is a decision tool, not a statistical method. The team should use the framework to select the statistical approach, but the team should also consult a statistician for the sample size calculation and the data analysis.

The framework is not a substitute for the method validation. The team should ensure that the method is validated in the receiving laboratory before the transfer study begins. The validation should include a precision study, an accuracy study, and a linearity study.

The framework is not a substitute for the biosafety review. The team should review the biosafety guidelines and ensure that both laboratories follow the appropriate practices. The biosafety review should be documented in the transfer protocol.

The framework is not a substitute for the regulatory review. The team should ensure that the transfer study complies with the applicable regulations and the quality system requirements. The regulatory review should be documented in the transfer protocol.

The framework is a practical tool that helps the team make a decision. The framework should be used in conjunction with the statistical methods and the professional judgment of the team.

Frequently Asked Questions

What is the difference between difference testing and equivalence testing?

Difference testing asks whether two laboratories produce different results. Equivalence testing asks whether the difference between the laboratories falls within a pre-defined acceptable range. Equivalence testing is the correct approach for method transfer because it directly tests the question of whether the receiving laboratory performs acceptably.

How do I choose the equivalence limits for a method transfer?

The equivalence limits should be based on the method's validation data and the clinical or analytical requirements of the result. The limits should be expressed as a percentage of the mean result or a multiple of the method's standard deviation. The limits must be pre-specified in the transfer protocol before the data are collected.

What is the TOST procedure?

The TOST procedure is a statistical method for equivalence testing. It constructs a confidence interval for the difference between two methods and checks whether the interval falls within the pre-defined equivalence limits. If the interval falls within the limits, the methods are declared equivalent.

How many samples do I need for a method transfer study?

The sample size depends on the equivalence limits, the expected variability of the method, and the desired power of the study. The sample size should be calculated before the study begins using a statistical software package.

What should I do if the transfer study fails?

If the transfer study fails, the team should investigate the cause of the difference. The investigation should include a review of the method, the equipment, the reagents, and the operators. The team should also consider whether the acceptance criteria were appropriate for the method.

Can I use a regression approach for method transfer?

Yes, a regression approach can be used to compare the results from the two laboratories. The slope and intercept of the regression line are compared to the expected values of 1 and 0. This approach is useful for detecting proportional and constant biases.

What is the role of the MIQE guidelines in method transfer?

The MIQE guidelines provide a framework for reporting and interpreting quantitative PCR experiments. The guidelines emphasize the importance of controls, transparency, and the documentation of the experimental conditions. These principles apply to method transfer, where the documentation of the protocol and the acceptance criteria is essential.

What biosafety considerations apply to method transfer?

The transfer of biological materials requires biosafety precautions. The team should review the CDC and NIH biosafety guidelines and ensure that both laboratories follow the appropriate practices. The team should also consider the shipping requirements for the biological materials.

Using the Evidence

Related Diagnostic Guides

• Method Validation in Clinical Laboratories: A Practical Protocol
• Biological Waste Management in Diagnostic Laboratories: A Practical Guide
• Biosafety Levels 1-4: A Decision Framework for Diagnostic Laboratories
• Biosafety Manual Development: A Template for Diagnostic Laboratories
• Biosafety Program Implementation: A Roadmap for Diagnostic Laboratories

References and Further Reading

• Molecular Biology and Laboratory Methods. National Library of Medicine.
• Biosafety in Microbiological and Biomedical Laboratories. CDC and NIH.
• NIH Guidelines for Recombinant or Synthetic Nucleic Acid Molecules. National Institutes of Health.
• Addgene Protocols. Addgene.
• MIQE Guidelines. Clinical Chemistry.
• NIST Laboratory Programs. National Institute of Standards and Technology.
• Laboratory Biosafety Manual. World Health Organization.
• NCBI Literature Resources. National Center for Biotechnology Information.
• Standardized methods for the determination of antioxidant capacity and phenolics in foods and dietary supplements.. Journal of agricultural and food chemistry, 2005.
• Analytical method transfer using equivalence tests with reasonable acceptance criteria and appropriate effort: extension of the ISPE concept.. Journal of pharmaceutical and biomedical analysis, 2010.
• Preimplantation genetic testing for aneuploidy versus morphology as selection criteria for single frozen-thawed embryo transfer in good-prognosis patients: a multicenter randomized clinical trial.. Fertility and sterility, 2019.

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