Measurement Uncertainty for Qualitative Assays

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-21

Quick Answer

• Qualitative assays report presence or absence, yet each result carries uncertainty that can be expressed as probability of a false positive or false negative.
• Laboratories should estimate uncertainty from validation data including sensitivity, specificity, repeatability, and reproducibility instead of reporting qualitative results as absolute.
• The main limitation is that no single universal formula exists for qualitative uncertainty, so each laboratory must document its own approach using available standards.

At a Glance

Understanding Qualitative Assay Uncertainty

Qualitative assays answer a binary question. A sample is reported as positive or negative, present or absent, reactive or nonreactive. Common examples include antigen detection tests, nucleic acid amplification tests, antibody screening assays, and culture-based presence or absence tests. Unlike quantitative measurements that produce a numerical value with units, qualitative results appear categorical. This appearance of simplicity can mislead laboratory professionals into thinking uncertainty does not apply.

Measurement uncertainty applies to every measurement procedure, including those that produce categorical results. The International Organization for Standardization standard ISO 15189 requires that laboratories determine measurement uncertainty for procedures used to report measured quantity values on patient samples. For numeric data, uncertainty can be expressed as standard deviation or coefficient of variation. For qualitative assays, the situation is considerably more difficult because the result is a classification instead of a quantity.

The immunology laboratory perspective illustrates this challenge clearly. Many immunology assays are reported as semi-quantitative results such as antibody titers or as qualitative positive or negative results. In the qualitative context, measuring uncertainty is considerably more difficult, yet strategies exist to minimize uncertainty. Parameters that contribute to uncertain measurements include bias, precision, standard uncertainty, sensitivity, specificity, repeatability, reproducibility, and verification. Traceability and standardization are closely linked to these parameters. The immunology laboratory perspective published in the Annals of Clinical Biochemistry explores these challenges and notes that many apply equally to other disciplines working with qualitative or semi-quantitative data.

The core problem is that a qualitative result is a decision based on a continuous underlying signal. A sample with a very strong signal is almost certainly positive. A sample with no detectable signal is almost certainly negative. But samples near the threshold or cut-off produce results that may vary between runs, between operators, or between reagent lots. The uncertainty of a qualitative result is therefore the probability that the classification is wrong.

Core Principles of Qualitative Uncertainty

The Decision Threshold Concept

Every qualitative assay has an underlying continuous measurement that is compared to a threshold. In a nucleic acid amplification test, the threshold may be a cycle threshold value. In an immunoassay, the threshold may be an optical density reading. In a culture-based test, the threshold may be the presence of visible colony growth after a specified incubation period.

The threshold is established during assay development and validation. Samples with signals clearly above the threshold are classified as positive. Samples with signals clearly below the threshold are classified as negative. The uncertainty arises because the signal from a given sample is not perfectly reproducible. A sample with a true signal near the threshold may produce a signal above the threshold in one run and below the threshold in another run.

This concept is fundamental to understanding qualitative uncertainty. The probability of a false result is highest for samples with true signals near the threshold and decreases as the true signal moves away from the threshold in either direction.

Probability of False Results

The probability of a false positive is the chance that a truly negative sample produces a signal above the threshold. The probability of a false negative is the chance that a truly positive sample produces a signal below the threshold. These probabilities are related to the analytical sensitivity and specificity of the assay.

Analytical sensitivity refers to the ability of the assay to detect small amounts of the target. Analytical specificity refers to the ability of the assay to distinguish the target from other substances that may be present. Both parameters are determined during method validation and contribute directly to the uncertainty of qualitative results.

For a laboratory reporting qualitative results, the uncertainty can be expressed as the probability of a false result. This probability can be estimated from validation data. If an assay has a sensitivity of 98 percent, then approximately 2 percent of truly positive samples will be reported as negative. If the assay has a specificity of 99 percent, then approximately 1 percent of truly negative samples will be reported as positive.

Confidence Intervals for Cut-offs

Another approach to expressing qualitative uncertainty involves confidence intervals around the cut-off. The cut-off is the signal value that separates positive from negative classifications. The uncertainty of the cut-off itself can be estimated from repeated measurements of control samples near the threshold.

If a laboratory measures a control sample with a signal near the cut-off multiple times, the distribution of those measurements provides information about the uncertainty of the classification. A confidence interval around the cut-off can be constructed. Samples with signals within this confidence interval have uncertain classifications. Samples with signals outside this interval have more certain classifications.

This approach is particularly useful for assays where the cut-off is a continuous value, such as an optical density or a cycle threshold. The laboratory can define a borderline zone around the cut-off and report results within this zone as indeterminate or equivocal.

ISO 19036 and Qualitative Uncertainty

ISO 19036 provides guidance for measurement uncertainty in microbiological testing. This standard addresses the unique challenges of qualitative microbiological methods, including presence or absence tests. The standard recognizes that traditional quantitative uncertainty approaches do not directly apply to qualitative results.

The ISO 19036 approach considers the probability of misclassification as the expression of uncertainty for qualitative methods. This probability is estimated from validation studies that establish the sensitivity and specificity of the method. The standard also addresses the contribution of sampling uncertainty and the uncertainty associated with the reference method used for validation.

For laboratories performing qualitative microbiological testing, ISO 19036 offers a structured framework for estimating and expressing uncertainty. The standard emphasizes that uncertainty estimates should be based on data generated by the laboratory itself instead of adopted from published values without verification.

Practical Workflow for Estimating Qualitative Uncertainty

Step 1: Review Validation Data

The first step is to review the data generated during method validation. This data should include sensitivity and specificity estimates, repeatability studies, and reproducibility studies. If the validation data is incomplete, additional studies may be needed before uncertainty can be estimated.

Review the validation report for the assay. Identify the number of true positive samples tested, the number of true negative samples tested, and the number of correct classifications for each group. Calculate the sensitivity and specificity from these numbers. Record the confidence intervals for these estimates.

Step 2: Assess Repeatability

Repeatability refers to the variation in results when the same sample is tested multiple times under the same conditions. For qualitative assays, repeatability is assessed by testing stable control samples repeatedly and calculating the proportion of concordant results.

Select at least three control samples for repeatability assessment. One control should have a signal clearly above the cut-off. One control should have a signal clearly below the cut-off. One control should have a signal near the cut-off. Test each control at least ten times under the same conditions. Record the number of concordant results for each control.

The control near the cut-off will typically show the lowest concordance. This concordance rate provides a direct estimate of the probability of misclassification for samples in that signal range.

Step 3: Assess Reproducibility

Reproducibility refers to the variation in results across different runs, operators, or laboratories. For qualitative assays, reproducibility is assessed by testing the same samples under different conditions and comparing the classifications.

If the laboratory participates in proficiency testing programs, the results from these programs provide reproducibility data. If proficiency testing is not available, the laboratory can conduct an internal reproducibility study by having multiple operators test the same panel of samples on different days.

Record the proportion of concordant classifications across the different conditions. This proportion provides an estimate of the reproducibility of the qualitative classification.

Step 4: Estimate Probability of False Results

Combine the sensitivity and specificity estimates with the repeatability and reproducibility data to estimate the probability of false results for the assay. This probability is the expression of uncertainty for the qualitative method.

For a sample with a signal clearly above the cut-off, the probability of a false negative is approximately equal to one minus the sensitivity. For a sample with a signal clearly below the cut-off, the probability of a false positive is approximately equal to one minus the specificity. For a sample with a signal near the cut-off, the probability of misclassification is higher and should be estimated from the repeatability data for the near-cut-off control.

Step 5: Document the Uncertainty Estimate

Document the uncertainty estimate in the laboratory quality manual or method file. Include the data used to derive the estimate, the calculations performed, and the date of the assessment. Review and update the uncertainty estimate whenever the method is changed or when new validation data becomes available.

The documentation should state the probability of false positive and false negative results for the assay. It should also describe the borderline zone around the cut-off and the laboratory policy for reporting results within this zone.

Options and Tradeoffs in Uncertainty Estimation

Probability-Based Approach

The probability-based approach expresses uncertainty as the probability of a false result. This approach is straightforward and directly useful for interpreting individual results. A clinician or researcher can use the probability to gauge confidence in a reported result.

The main limitation is that the probability applies to the assay as a whole instead of to a specific sample. A sample with a very strong signal has a lower probability of being misclassified than a sample with a signal near the cut-off. The overall sensitivity and specificity do not capture this difference.

Confidence Interval Approach

The confidence interval approach expresses uncertainty as a range of signal values around the cut-off. This approach provides more information about the confidence in individual results. Samples with signals far from the cut-off have high confidence. Samples with signals within the confidence interval have low confidence.

The main limitation is that this approach requires more data and more complex calculations. The laboratory must have sufficient measurements of near-cut-off controls to construct a reliable confidence interval.

Borderline Zone Approach

The borderline zone approach defines a range of signal values around the cut-off as indeterminate. Results within this zone are reported as equivocal or indeterminate instead of positive or negative. This approach reduces the probability of false results by avoiding classification of uncertain samples.

The main limitation is that indeterminate results may not be clinically useful. The requesting provider must decide how to act on an indeterminate result, which may require additional testing or a different sample.

Comparison of Approaches

Records and Measurements

Essential Records for Uncertainty Estimation

The laboratory should maintain records that support the uncertainty estimate for each qualitative assay. These records include the validation report, repeatability study data, reproducibility study data, and proficiency testing results.

The validation report should document the sensitivity and specificity of the assay, the number of samples tested, and the characteristics of the sample panel. The repeatability study data should document the concordance rates for each control sample. The reproducibility study data should document the concordance rates across different conditions. Proficiency testing results should document the performance of the assay relative to other laboratories.

Measurements to Collect

For each qualitative assay, the laboratory should collect the following measurements:

• Sensitivity expressed as a percentage with confidence interval
• Specificity expressed as a percentage with confidence interval
• Repeatability concordance for a strong positive control
• Repeatability concordance for a weak positive control near the cut-off
• Repeatability concordance for a negative control
• Reproducibility concordance across operators or runs
• Proficiency testing scores if available

These measurements provide the data needed to estimate the probability of false results and to identify the signal range where misclassification is most likely.

Review Schedule

The uncertainty estimate should be reviewed at least annually or whenever the method changes. Changes that trigger a review include new reagent lots, new equipment, changes to the testing protocol, and changes in the sample matrix. The review should confirm that the existing uncertainty estimate remains valid or should update the estimate based on new data.

Common Failure Patterns

Treating Qualitative Results as Absolute

A common failure is treating qualitative results as absolute truths. A positive result is reported without any indication of the probability that it could be false. This practice ignores the inherent uncertainty of the measurement and can lead to incorrect clinical or research decisions.

The laboratory should include information about the uncertainty of qualitative results in the report or in the method documentation. At minimum, the laboratory should be able to state the probability of false results for the assay.

Ignoring Near-Cut-off Samples

Another common failure is ignoring the special uncertainty of samples with signals near the cut-off. These samples have a higher probability of misclassification than samples with signals far from the cut-off. Laboratories that do not test near-cut-off controls during validation may underestimate the uncertainty of the assay.

The laboratory should include near-cut-off controls in validation and quality control testing. The concordance rate for these controls provides the most relevant estimate of uncertainty for the most uncertain samples.

Using Published Uncertainty Values Without Verification

Some laboratories adopt uncertainty values from published studies or manufacturer claims without verifying these values for their own conditions. This practice is problematic because uncertainty depends on the specific laboratory conditions, equipment, reagents, and operators.

The laboratory should generate its own uncertainty data using its own conditions. Published values can be used as a starting point but must be verified with local data.

Failing to Update Uncertainty Estimates

Uncertainty estimates can become outdated when methods change. A laboratory that continues to use an old uncertainty estimate after changing reagents or equipment may be reporting inaccurate uncertainty information.

The laboratory should review and update uncertainty estimates whenever the method changes. The review should include new repeatability and reproducibility studies as needed.

Confusing Analytical and Clinical Uncertainty

Analytical uncertainty refers to the variation in the measurement itself. Clinical uncertainty refers to the uncertainty of the clinical interpretation of the result. These are different concepts and should not be confused.

The laboratory is responsible for estimating and reporting analytical uncertainty. The clinical interpretation of the result, including the clinical significance of a false positive or false negative, is the responsibility of the requesting provider.

Quality Controls for Qualitative Assays

Internal Quality Control

Internal quality control for qualitative assays should include positive and negative controls in each run. The positive control should produce a signal clearly above the cut-off. The negative control should produce a signal clearly below the cut-off. A weak positive control near the cut-off should be included when available.

The results of these controls should be recorded and monitored over time. A shift in the signal of the weak positive control may indicate a change in assay performance that increases uncertainty. The laboratory should investigate any control failure before reporting patient or sample results.

External Quality Assessment

External quality assessment, also known as proficiency testing, provides an independent assessment of assay performance. Participation in external quality assessment programs allows the laboratory to compare its results with those of other laboratories using the same or similar methods.

The results of external quality assessment provide reproducibility data that can be used in the uncertainty estimate. Discrepant results in external quality assessment should be investigated to identify the cause and to determine whether the uncertainty estimate needs to be updated.

Verification of New Reagent Lots

New reagent lots should be verified before being placed into routine use. The verification should include testing of positive and negative controls and comparison of results with the previous reagent lot. The verification data should be recorded and reviewed to confirm that assay performance has not changed.

If the new reagent lot produces different control signals or different classification results, the uncertainty estimate should be reviewed and updated as needed.

Biosafety and Regulatory Context

Biosafety Considerations

Qualitative assays may involve the handling of biological samples that contain infectious agents. The biosafety level required for the assay depends on the nature of the samples and the agents being detected. The Biosafety in Microbiological and Biomedical Laboratories publication from the CDC and NIH provides guidance on biosafety principles, risk assessment, containment, and institutional oversight.

Laboratories should conduct a risk assessment for each qualitative assay and implement appropriate containment measures. The World Health Organization Laboratory Biosafety Manual provides additional guidance on biosafety practices for laboratories handling biological materials.

Regulatory Requirements

The ISO 15189 standard requires that laboratories determine measurement uncertainty for procedures used to report measured quantity values on patient samples. This requirement applies to qualitative assays as well as quantitative assays. The laboratory must document its approach to uncertainty estimation for qualitative methods.

For research laboratories, the NIH Guidelines for Recombinant or Synthetic Nucleic Acid Molecules may apply if the qualitative assay involves recombinant or synthetic nucleic acids. These guidelines provide oversight context for research involving such molecules.

Professional Escalation Criteria

The laboratory should have criteria for escalating uncertainty-related issues to appropriate personnel. Escalation is warranted when:

• The probability of false results exceeds the level established by the laboratory for the intended use of the assay
• Quality control results indicate a change in assay performance that increases uncertainty
• Proficiency testing results show a significant discrepancy from expected performance
• A method change produces unexpected changes in control signals or classification results

The escalation should include documentation of the issue, the investigation performed, and the actions taken to address the issue.

Measurement Uncertainty in Specific Qualitative Methods

Nucleic Acid Amplification Tests

Nucleic acid amplification tests detect the presence of specific nucleic acid sequences. These tests are widely used for pathogen detection and genetic testing. The cycle threshold value provides a continuous signal that is compared to a cut-off for qualitative classification.

The uncertainty of nucleic acid amplification tests depends on the efficiency of the amplification reaction, the quality of the nucleic acid extraction, and the presence of inhibitors in the sample. The MIQE guidelines published in Clinical Chemistry provide recommendations for qPCR reporting, controls, transparency, and interpretation. These guidelines emphasize the importance of reporting the efficiency of the amplification reaction and the limit of detection.

For qualitative nucleic acid amplification tests, the uncertainty can be expressed as the probability of a false negative for samples with target concentrations near the limit of detection. The laboratory should determine the limit of detection during validation and should test samples near this limit to estimate the probability of misclassification.

Immunoassays

Immunoassays detect the presence of antibodies or antigens in samples. These tests produce a signal that is compared to a cut-off for qualitative classification. The signal may be an optical density, a fluorescence value, or a chemiluminescence value.

The uncertainty of immunoassays depends on the binding affinity of the antibodies, the concentration of the target, and the presence of cross-reacting substances. The immunology laboratory perspective highlights the challenges of expressing uncertainty for qualitative and semi-quantitative immunoassays and notes that strategies exist to minimize uncertainty through careful attention to bias, precision, sensitivity, specificity, repeatability, reproducibility, and verification.

For qualitative immunoassays, the uncertainty can be expressed as the probability of a false positive or false negative result. The laboratory should determine the sensitivity and specificity of the assay during validation and should monitor the signal of controls over time to detect changes in assay performance.

Culture-Based Presence or Absence Tests

Culture-based tests detect the presence of viable microorganisms in a sample. These tests involve inoculation of the sample into or onto a culture medium, incubation under appropriate conditions, and examination for growth.

The uncertainty of culture-based tests depends on the viability of the target organism, the adequacy of the sample, the selectivity of the culture medium, and the incubation conditions. The ISO 19036 standard provides guidance for measurement uncertainty in microbiological testing, including qualitative methods.

For culture-based tests, the uncertainty can be expressed as the probability of a false negative result. This probability depends on the sensitivity of the culture method, which is influenced by the number of viable organisms in the sample and the ability of the method to support their growth.

Practical Implementation Steps

Step 1: Select the Uncertainty Approach

Select the approach for expressing uncertainty based on the assay type, the available data, and the intended use of the results. The probability-based approach is simplest and is appropriate for most qualitative assays. The confidence interval approach provides more information but requires more data. The borderline zone approach is useful for assays where false results have serious consequences.

Step 2: Gather Validation Data

Gather the validation data needed to support the uncertainty estimate. This data includes sensitivity and specificity estimates, repeatability data, and reproducibility data. If the existing validation data is insufficient, conduct additional studies to generate the needed data.

Step 3: Calculate the Uncertainty Estimate

Calculate the uncertainty estimate using the selected approach. For the probability-based approach, calculate the probability of false positive and false negative results from the sensitivity and specificity data. For the confidence interval approach, calculate the confidence interval around the cut-off from the repeatability data for near-cut-off controls.

Step 4: Document the Uncertainty Estimate

Document the uncertainty estimate in the method file or quality manual. Include the data used, the calculations performed, and the date of the assessment. Describe the borderline zone if one is defined and the laboratory policy for reporting results within this zone.

Step 5: Implement Quality Controls

Implement quality controls that support the uncertainty estimate. Include positive, negative, and near-cut-off controls in each run. Monitor control signals over time to detect changes in assay performance. Investigate any control failures before reporting results.

Step 6: Review and Update

Review the uncertainty estimate at least annually or whenever the method changes. Update the estimate based on new validation data, quality control data, or proficiency testing results. Document the review and any changes to the estimate.

Common Questions in Implementation

How Much Data Is Needed?

The amount of data needed depends on the approach selected and the required confidence in the estimate. For the probability-based approach, the validation data should include a sufficient number of true positive and true negative samples to provide a stable estimate of sensitivity and specificity. For the confidence interval approach, repeated measurements of near-cut-off controls are needed to construct a reliable interval.

What If the Assay Has High Uncertainty?

If the uncertainty estimate indicates a high probability of false results, the laboratory should investigate the causes. Possible causes include poor assay design, inadequate validation, operator variation, or reagent problems. The laboratory should address the causes and repeat the uncertainty assessment after corrective actions.

How Should Borderline Results Be Reported?

Borderline results should be reported according to the laboratory policy. The policy should be documented and should specify whether borderline results are reported as indeterminate, equivocal, or with a comment indicating the uncertainty. The requesting provider should be informed of the meaning of borderline results.

Can Uncertainty Be Reduced?

Uncertainty can be reduced by improving assay performance, standardizing procedures, and using more precise equipment. However, some uncertainty is inherent in any measurement and cannot be eliminated. The goal is to estimate uncertainty accurately and to communicate it clearly.

Limitations of Qualitative Uncertainty Estimation

No Universal Formula

There is no single universal formula for estimating uncertainty in qualitative assays. Each laboratory must select an approach and generate data to support the estimate. This lack of standardization can lead to inconsistent uncertainty estimates across laboratories.

Dependence on Validation Data Quality

The quality of the uncertainty estimate depends on the quality of the validation data. If the validation data is based on a small number of samples or samples that do not represent the intended use, the uncertainty estimate may be inaccurate.

Difficulty with Rare Targets

For assays that detect rare targets, the number of true positive samples available for validation may be limited. This limitation can make it difficult to estimate sensitivity accurately and to assess the uncertainty of positive results.

Changing Assay Performance

Assay performance can change over time due to reagent lot changes, equipment drift, or operator variation. The uncertainty estimate reflects the performance at the time of the assessment and may not accurately reflect current performance.

Safety and Professional Escalation

Safety Considerations

Laboratories performing qualitative assays should follow appropriate biosafety practices. The Biosafety in Microbiological and Biomedical Laboratories publication from the CDC and NIH provides guidance on biosafety principles, risk assessment, containment, and institutional oversight. The World Health Organization Laboratory Biosafety Manual provides additional guidance.

The laboratory should conduct a risk assessment for each assay and implement appropriate containment measures. Personnel should receive training on biosafety practices and should follow standard precautions when handling biological samples.

Professional Escalation Criteria

The laboratory should have clear criteria for escalating uncertainty-related issues. Escalation is warranted when the uncertainty estimate indicates a level of false results that is unacceptable for the intended use, when quality control results indicate a change in assay performance, or when proficiency testing results show significant discrepancies.

The escalation should be documented and should include the issue, the investigation, and the actions taken. The laboratory director or quality manager should be informed of significant uncertainty-related issues.

Decision Framework for Selecting an Uncertainty Expression Method

Laboratories often struggle to choose which uncertainty expression approach fits their specific qualitative assay. The existing comparison of probability-based, confidence interval, and borderline zone approaches describes their features but does not provide a structured decision process. This section presents a practical decision framework that guides laboratories through the selection based on assay characteristics, available data, intended use, and regulatory requirements.

Step 1: Classify the Assay Output Type

Begin by classifying the qualitative assay into one of three output categories. This classification determines which uncertainty expression methods are technically feasible.

Category A: Binary classification from a continuous signal. The assay produces a numerical signal such as optical density, cycle threshold, or fluorescence value that is compared to a fixed cut-off. Examples include enzyme-linked immunosorbent assays, nucleic acid amplification tests, and lateral flow devices with reader systems. These assays support all three uncertainty expression approaches.

Category B: Binary classification from visual inspection. The assay produces a visual result such as color change, agglutination, or colony growth that is interpreted by an operator. Examples include rapid antigen tests read by eye, latex agglutination tests, and culture-based presence or absence tests. These assays do not produce a continuous signal that can be used for confidence interval construction. The probability-based approach and borderline zone approach remain feasible if the laboratory defines the borderline zone through replicate testing of characterized samples.

Category C: Semi-quantitative results reported as categories. The assay produces tiered results such as antibody titers reported as 1:40, 1:80, 1:160, or graded reactions reported as 1 plus, 2 plus, or 3 plus. These assays sit between qualitative and quantitative methods. The immunology laboratory perspective notes that semi-quantitative results present distinct uncertainty challenges because each titer step represents a range of underlying concentrations. For these assays, the laboratory should express uncertainty as the probability that the reported category differs from the true category by one or more steps.

Record the assay output category in the method file. This classification drives the remainder of the decision framework.

Step 2: Assess Data Availability and Quality

Evaluate whether the laboratory has sufficient data to support each uncertainty expression approach. The data requirements differ substantially across approaches.

For the probability-based approach, the laboratory needs sensitivity and specificity estimates from validation studies. These estimates should include confidence intervals to reflect the uncertainty of the estimates themselves. A validation study with 20 true positive samples and 20 true negative samples produces wider confidence intervals than a study with 200 samples in each group. The laboratory should record the sample sizes used to derive sensitivity and specificity.

For the confidence interval approach, the laboratory needs repeated measurements of control samples with signals near the cut-off. The NIST Laboratory Programs emphasize that measurement science depends on understanding the behavior of measurement systems through repeated observations. A minimum of ten replicate measurements of a near-cut-off control is a practical starting point, but more replicates produce a more stable confidence interval. The laboratory should document the number of replicates and the distribution of results.

For the borderline zone approach, the laboratory needs data that defines the signal range where misclassification probability exceeds an acceptable threshold. This data comes from replicate testing of multiple control samples spanning the cut-off region. The laboratory should test controls at several signal levels above and below the cut-off to map the relationship between signal strength and classification concordance.

If the laboratory lacks sufficient data for a preferred approach, it must either generate the data through additional studies or select a different approach that the existing data can support.

Step 3: Consider the Intended Use of Results

The intended use of the qualitative result determines how much uncertainty is acceptable and which expression format is most useful to the result consumer.

For clinical diagnostic use, the requesting provider needs to understand the confidence in a positive or negative result. The probability-based approach communicates this directly. A result reported with a 2 percent probability of being false negative gives the provider actionable information. The borderline zone approach also serves clinical use by identifying samples that require additional testing or a fresh sample collection.

For regulatory or compliance testing, such as food safety presence or absence testing, the ISO 19036 approach applies. This standard addresses measurement uncertainty in microbiological testing and considers the probability of misclassification as the expression of uncertainty for qualitative methods. Laboratories performing regulatory microbiological testing should align their uncertainty expression with the expectations of the relevant regulatory program.

For research applications, the confidence interval approach may be more useful because researchers often need to understand the signal distribution around the cut-off to interpret borderline results in the context of their study population.

Step 4: Evaluate Practical Implementation Burden

Each approach carries different implementation burdens in terms of ongoing quality control, data analysis, and reporting complexity.

The probability-based approach requires the least ongoing burden. Once sensitivity and specificity are established from validation data, the laboratory reports the probability of false results with each qualitative result or includes it in the method documentation. The main ongoing requirement is periodic verification that assay performance has not changed.

The confidence interval approach requires ongoing collection of near-cut-off control data. Each quality control run should include a near-cut-off control, and the laboratory must track the distribution of signals over time. This approach generates more data and requires more statistical analysis.

The borderline zone approach requires the laboratory to define and maintain the borderline zone. The zone must be verified when reagent lots change or when equipment is recalibrated. The laboratory must also establish a policy for reporting results within the zone, which may include issuing indeterminate reports or adding interpretive comments.

Step 5: Apply the Decision Matrix

Use the following decision matrix to select the primary uncertainty expression approach. The matrix combines the assay output category, data availability, intended use, and implementation burden.

The laboratory should document the rationale for its selected approach in the quality manual. This documentation should include the assay output category, the data available at the time of selection, the intended use of results, and the implementation burden considered.

Step 6: Establish Review Triggers

The selected uncertainty expression approach should be reviewed when specific triggers occur. These triggers differ from the general annual review schedule and represent events that may invalidate the basis for the selected approach.

Review the approach when the assay output category changes. For example, if a laboratory upgrades from visual reading to a reader system that produces continuous signals, the confidence interval approach becomes feasible and may be preferable.

Review the approach when new validation data becomes available. A larger validation study may produce sensitivity and specificity estimates with narrower confidence intervals, which may change the preferred approach.

Review the approach when the intended use changes. If a research assay is repurposed for clinical use, the uncertainty expression may need to change to meet clinical communication needs.

Review the approach when regulatory requirements change. The NIH Guidelines for Recombinant or Synthetic Nucleic Acid Molecules and other oversight frameworks may impose specific reporting expectations that affect the uncertainty expression approach.

Common Decision Errors

Laboratories commonly make three errors when selecting an uncertainty expression approach.

The first error is selecting the confidence interval approach without sufficient near-cut-off data. This approach requires repeated measurements of controls near the threshold. Without this data, the confidence interval is unstable and may mislead result consumers. The laboratory should verify that it has at least ten replicate measurements of a near-cut-off control before adopting this approach.

The second error is applying the borderline zone approach without defining the zone based on data. Some laboratories define a borderline zone based on a fixed percentage above and below the cut-off without testing whether this zone actually captures the samples with elevated misclassification probability. The zone should be defined from replicate testing data that shows where classification concordance drops below an acceptable level.

The third error is using the same uncertainty expression for all assays in the laboratory without considering assay-specific characteristics. A visual read rapid test and an instrument-read nucleic acid amplification test have different uncertainty structures and may warrant different expression approaches. The laboratory should apply the decision framework to each assay individually.

Documentation Requirements

The decision framework produces documentation that supports the laboratory's uncertainty expression choice. This documentation should include the assay output category classification, the data availability assessment, the intended use analysis, the implementation burden evaluation, and the rationale for the selected approach.

The documentation should also record the review triggers and the dates of any reviews. This record demonstrates that the laboratory has systematically considered the uncertainty expression approach and has a basis for its selection. The MIQE guidelines emphasize the importance of transparency in reporting and interpretation for nucleic acid based methods, and the same principle applies to documenting uncertainty expression decisions.

The laboratory should store this documentation with the method file or quality manual and should make it available for audit or inspection. The documentation should be updated whenever the approach is reviewed or changed.

Frequently Asked Questions

What is measurement uncertainty for a qualitative assay?

Measurement uncertainty for a qualitative assay is the probability that the reported positive or negative classification is incorrect. It arises because the underlying signal that determines the classification is not perfectly reproducible, especially for samples with signals near the cut-off.

How is uncertainty expressed for qualitative results?

Uncertainty for qualitative results can be expressed as the probability of a false positive or false negative result, as a confidence interval around the cut-off, or as a defined borderline zone where results are reported as indeterminate.

Why is uncertainty harder to estimate for qualitative assays than for quantitative assays?

Quantitative assays produce numerical values that can be expressed with standard deviation or coefficient of variation. Qualitative assays produce categorical results, so uncertainty must be expressed as the probability of misclassification, which requires different data and calculations.

What data is needed to estimate qualitative uncertainty?

The data needed includes sensitivity and specificity from validation studies, repeatability data from repeated testing of control samples, and reproducibility data from testing across different conditions. Near-cut-off controls are especially important for estimating the probability of misclassification.

How does ISO 19036 address qualitative uncertainty?

ISO 19036 provides guidance for measurement uncertainty in microbiological testing, including qualitative presence or absence methods. The standard considers the probability of misclassification as the expression of uncertainty for qualitative methods.

What should a laboratory do if uncertainty is high?

The laboratory should investigate the causes of high uncertainty, which may include assay design issues, inadequate validation, operator variation, or reagent problems. Corrective actions should be implemented and the uncertainty assessment should be repeated.

How often should uncertainty estimates be reviewed?

Uncertainty estimates should be reviewed at least annually or whenever the method changes. Changes that trigger a review include new reagent lots, new equipment, changes to the testing protocol, and changes in the sample matrix.

Can borderline results be reported as indeterminate?

Yes, laboratories can define a borderline zone around the cut-off and report results within this zone as indeterminate or equivocal. This approach reduces the probability of false results but may complicate clinical interpretation.

Related Diagnostic Guides

• Measurement Uncertainty in Laboratory Medicine: A Guide to Calculation and Reporting
• Biological Waste Management in Diagnostic Laboratories: A Practical Guide
• Method Validation in Clinical Laboratories: A Practical Protocol
• Laboratory Quality Management Systems: A Practical Implementation Framework for Diagnostic Laboratories
• Nanotechnology in Rapid Viral Diagnostic Tests

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.
• Refining Mishel's Reconceptualized Uncertainty in Illness Theory: Explaining the Development of Uncertainty in Chronic Illness.. Research and theory for nursing practice, 2024.
• Creep level qualitative evaluating of crushed rock based on uncertainty measurement theory and hierarchical analysis.. Scientific reports, 2024.
• Uncertainty of measurement: an immunology laboratory perspective.. Annals of clinical biochemistry, 2015.

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