Precision and Accuracy in Immunoassay Validation

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

Quick Answer

Validate immunoassay precision by measuring repeatability within a single run and intermediate precision across separate runs, days, and operators using replicate samples.
Assess accuracy through spike recovery experiments and by comparing your assay results against a reference method or certified standard material.
Document every validation step in a written protocol with raw data, calculations, and acceptance criteria, because validation is only meaningful when it is reproducible and auditable.

At a Glance

Immunoassay validation is the process of demonstrating that a measurement procedure reliably and consistently quantifies the target analyte in a defined sample matrix. Precision and accuracy are the two foundational performance characteristics that must be evaluated before an assay is used for research, diagnostic, or regulatory purposes. Precision describes the closeness of agreement between repeated measurements of the same sample, while accuracy describes the closeness of agreement between the measured value and the true value or an accepted reference value.

Validation Parameter	What It Measures	Practical Assessment Method	Key Limitation
Intra-assay precision (repeatability)	Variability within a single assay run	Run 5 to 10 replicates of the same sample in one plate or batch and calculate the coefficient of variation	Does not capture day-to-day or operator-to-operator variability
Inter-assay precision (intermediate precision)	Variability across separate runs, days, or operators	Run the same sample on multiple days, with different operators or reagent lots, and calculate the combined CV	Requires careful tracking of run conditions and can be time-consuming
Accuracy (bias)	Closeness of the measured value to the true value	Spike a known amount of analyte into the sample matrix and measure the recovery percentage	Recovery can be affected by matrix effects, sample preparation, and the quality of the reference standard

Core Principles of Immunoassay Validation

Immunoassay validation is a systematic process that establishes the performance characteristics of a method before it is used to generate data. The two most important parameters are precision and accuracy, but they are not independent. A precise assay can be consistently wrong, and an accurate assay can be imprecise. Both must be evaluated together to demonstrate that the method is fit for its intended purpose.

Precision is expressed as the coefficient of variation (CV), which is the standard deviation divided by the mean, multiplied by 100. A lower CV indicates higher precision. The acceptable CV depends on the application, the analyte concentration, and the sample matrix. For research applications, a CV below 15% is often considered acceptable, but this threshold is not universal and must be defined in the validation protocol.

Accuracy is expressed as the percent recovery or the percent bias. Recovery is calculated by spiking a known amount of analyte into a sample and measuring the amount that is detected. Bias is the difference between the measured value and the true value, expressed as a percentage of the true value. Both metrics are used to assess whether the assay is producing results that are close to the true concentration.

The validation process must be documented in a written protocol that specifies the samples, the number of replicates, the statistical methods, and the acceptance criteria. The protocol should also define the conditions under which the assay is considered valid and the steps to take if the acceptance criteria are not met. This documentation is essential for reproducibility and for demonstrating that the assay is reliable to other researchers, reviewers, or regulatory bodies.

Core Principles of Precision Assessment

Precision is the degree of scatter among repeated measurements of the same sample. It is a measure of the random error of the assay. Precision is evaluated at two levels: within a single run (intra-assay) and across separate runs (inter-assay). Both levels are necessary because the variability of an assay can change depending on the conditions under which it is performed.

Intra-Assay Precision

Intra-assay precision, also called repeatability, is the variability observed when the same sample is measured multiple times within the same assay run. This type of precision is assessed by running several replicates of the same sample on the same plate or in the same batch. The replicates should be prepared independently, meaning that each replicate is pipetted separately and processed through the entire assay procedure.

The number of replicates used for the intra-assay precision assessment should be sufficient to provide a reliable estimate of the variability. A common approach is to use at least 6 replicates, but the exact number depends on the assay and the expected variability. The replicates should be spread across the plate to account for positional effects, such as differences in temperature or evaporation across the plate.

The intra-assay CV is calculated from the mean and standard deviation of the replicate measurements. A low CV indicates that the assay is producing consistent results within a single run. If the CV is high, the assay may be affected by pipetting errors, reagent instability, or other factors that vary within the run.

Inter-Assay Precision

Inter-assay precision, also called intermediate precision, is the variability observed when the same sample is measured across separate assay runs. This type of precision is assessed by running the same sample on different days, with different operators, or with different reagent lots. The goal is to capture the variability that is introduced by the assay conditions that change between runs.

Inter-assay precision is more difficult to assess than intra-assay precision because it requires the assay to be run multiple times under different conditions. The sample must be stored under conditions that preserve its stability, and the assay conditions must be carefully controlled to ensure that the variability is due to the assay and not to the sample or the environment.

The inter-assay CV is calculated from the mean of the means of each run and the standard deviation of the run means. The inter-assay CV is typically higher than the intra-assay CV because it includes the variability from the different runs. The acceptable inter-assay CV depends on the application and the analyte concentration.

Inter-Laboratory Precision

Inter-laboratory precision is the variability observed when the same sample is measured in different laboratories. This type of precision is the most difficult to assess because it requires the coordination of multiple laboratories and the use of the same sample and the same protocol. Inter-laboratory precision is often assessed through proficiency testing programs or collaborative studies.

Inter-laboratory precision is important for assays that are used in multiple laboratories, such as clinical diagnostic assays or assays used in regulatory studies. The variability between laboratories can be due to differences in equipment, reagents, operators, and environmental conditions. The inter-laboratory CV is calculated from the results of the participating laboratories and is used to assess the robustness of the assay.

Core Principles of Accuracy Assessment

Accuracy is the closeness of the measured value to the true value of the analyte in the sample. Accuracy is assessed by two methods: spike recovery and comparison to a reference method. Both methods are used to determine whether the assay is producing results that are close to the true concentration.

Spike Recovery

Spike recovery is a method for assessing accuracy by adding a known amount of the analyte to a sample and measuring the recovery. The recovery is calculated as the measured concentration divided by the expected concentration, multiplied by 100. A recovery of 100% indicates that the assay is accurately measuring the analyte in the sample.

Spike recovery is performed by preparing a sample that contains a known concentration of the analyte. The sample is then spiked with a known amount of the analyte, and the concentration is measured. The recovery is calculated as the difference between the measured concentration and the concentration of the unspiked sample, divided by the spike concentration, multiplied by 100.

The spike recovery should be performed at multiple concentrations, including the low, medium, and high end of the assay range. The recovery should be consistent across the range, and the recovery should be within the acceptance criteria. The acceptance criteria for spike recovery are typically between 80% and 120%, but the exact criteria depend on the application and the analyte.

Comparison to a Reference Method

Comparison to a reference method is another method for assessing accuracy. The assay is used to measure a set of samples, and the results are compared to the results obtained with a reference method. The reference method is a method that is known to be accurate, such as a mass spectrometry method or a method that is traceable to a reference standard.

The comparison is performed by measuring the same set of samples with both methods and calculating the bias. The bias is the difference between the measured value and the reference value, expressed as a percentage of the reference value. The bias should be within the acceptance criteria defined in the validation protocol.

The comparison to a reference method is more rigorous than spike recovery because it assesses the accuracy of the assay across a range of concentrations and sample matrices. However, it requires a reference method that is accurate and that is available in the laboratory.

Practical Workflow for Validation

The validation of an immunoassay should be performed in a systematic manner. The following workflow provides a step-by-step approach to assessing precision and accuracy.

Step 1: Define the Validation Protocol

The first step is to define the validation protocol. The protocol should specify the purpose of the assay, the sample matrix, the concentration range, the number of replicates, the acceptance criteria, and the statistical methods. The protocol should also specify the conditions under which the assay is performed, such as the temperature, the incubation time, and the equipment.

The protocol should be written before the validation is performed and should be approved by the laboratory supervisor or the quality assurance team. The protocol should be followed exactly during the validation to ensure that the results are reproducible.

Step 2: Prepare the Samples

The samples for the validation should be prepared according to the protocol. The samples should be representative of the samples that will be measured in the routine use of the assay. The samples should be prepared in a matrix that is similar to the sample matrix, and the samples should be stored under the conditions that will be used for the routine samples.

The samples for the precision assessment should be prepared in replicates. The replicates should be prepared independently, and each replicate should be measured separately. The samples for the accuracy assessment should be prepared with a known concentration of the analyte, and the samples should be spiked with a known amount of the analyte.

Step 3: Run the Assay

The assay should be run according to the protocol. The assay should be run in the same manner as the routine samples, and the conditions should be controlled. The assay should be run in a single run for the intra-assay precision, and the assay should be run in multiple runs for the inter-assay precision.

The assay should be run with the appropriate controls, such as a blank, a standard curve, and a quality control sample. The controls should be used to verify that the assay is performing correctly, and the controls should be within the acceptance criteria.

Step 4: Calculate the Precision and Accuracy

The precision and accuracy should be calculated from the data. The precision is calculated as the CV, and the accuracy is calculated as the recovery or the bias. The calculations should be performed using the appropriate statistical methods, and the results should be recorded.

The precision and accuracy should be compared to the acceptance criteria defined in the protocol. If the results are within the acceptance criteria, the assay is considered to be valid. If the results are not within the acceptance criteria, the assay should be investigated, and the validation should be repeated.

Step 5: Document the Results

The results of the validation should be documented in a validation report. The report should include the protocol, the raw data, the calculations, and the results. The report should also include the acceptance criteria and the conclusions of the validation.

The validation report should be reviewed by the laboratory or the quality assurance team. The report should be stored in a secure location, and the report should be available for review by the auditors or the regulators.

Records and Measurements

The validation of an immunoassay requires the collection of a large amount of data. The data should be recorded in a systematic manner, and the data should be stored in a secure location. The data should include the following:

The date and time of the assay
The operator who performed the assay
The reagents and the lot numbers
The equipment and the calibration status
The sample and the sample preparation
The raw data from the assay
The calculations and the results

The data should be recorded in a laboratory notebook or in an electronic data system. The data should be recorded in a manner that is clear and that can be reviewed by the auditors. The data should be stored for the period of time that is required by the laboratory or the regulatory agency.

The records should be used to track the performance of the assay over time. The records should be reviewed periodically to identify any trends or changes in the assay performance. The records should be used to make decisions about the assay, such as whether the assay needs to be revalidated or whether the assay needs to be modified.

Common Failure Patterns in Validation

The validation of an immunoassay can fail for a number of reasons. The following are the common failure patterns that are observed in the validation of immunoassays.

High Intra-Assay Variability

High intra-assay variability is a common failure pattern. The variability can be caused by the assay, the reagents, the equipment, or the operator. The variability can be reduced by the following:

Using the same lot of reagents
Calibrating the equipment
Using the same operator
Controlling the temperature and the time
Using the same sample preparation

The variability should be investigated to identify the cause. The variability should be reduced to the level that is within the acceptance criteria.

Low Spike Recovery

Low spike recovery is a common failure pattern. The recovery can be caused by the sample matrix, the analyte, or the assay. The recovery can be improved by the following:

Using a different sample matrix
Using a different extraction method
Using a different assay
Using a different spike concentration

The recovery should be investigated to identify the cause. The recovery should be improved to the level that is within the acceptance criteria.

Inconsistent Inter-Assay Precision

Inconsistent inter-assay precision is a common failure pattern. The precision can be caused by the assay, the operator, or the equipment. The precision can be improved by the following:

Using the same operator
Using the same equipment
Using the same reagents
Controlling the conditions

The precision should be investigated to identify the cause. The precision should be improved to the level that is within the acceptance criteria.

Quality and Safety Context

The validation of an immunoassay is performed in a laboratory that is subject to the biosafety and the quality requirements. The laboratory should be operated in accordance with the biosafety principles that are described in the Biosafety in Microbiological and Biomedical Laboratories. The laboratory should be operated in accordance with the quality requirements that are described in the NIST Laboratory Programs.

The laboratory should be operated in a manner that is safe for the operator and the environment. The laboratory should be operated in a manner that is consistent with the NIH Guidelines for Recombinant or Synthetic Nucleic Acid Molecules if the assay involves the use of the recombinant or synthetic nucleic acid molecules.

The laboratory should be operated in a manner that is consistent with the Molecular Biology and Laboratory Methods and the Addgene Protocols for the laboratory methods. The laboratory should be operated in a manner that is consistent with the MIQE Guidelines for the reporting of the assay.

Limitations and Professional Escalation

The validation of an immunoassay is a complex process that requires the expertise of the laboratory. The validation is limited by the following:

The sample matrix
The analyte concentration
The assay
The equipment
The operator

The validation should be performed by the laboratory that has the expertise and the experience. The validation should be performed in accordance with the protocol and the acceptance criteria. The validation should be reviewed by the quality assurance team.

If the validation results are not within the acceptance criteria, the validation should be escalated to the quality supervisor or the laboratory director. The escalation should include the data and the results. The escalation should be used to identify the cause of the failure and to determine the corrective action.

Decision Framework for Selecting Precision and Accuracy Experiments

Validation resources are finite, and running every possible precision and accuracy experiment for every analyte in every matrix is rarely practical. A structured decision framework helps you allocate effort where it matters most. The framework below prioritizes experiments based on assay purpose, sample matrix complexity, and regulatory or publication requirements. It is not a substitute for a written validation protocol, but it helps you design one that is proportionate to the risk of assay failure.

Step 1: Classify the Assay by Intended Use

Start by writing a one sentence statement of what the assay results will be used for. This statement drives every subsequent decision. Classify the assay into one of three tiers.

Tier 1 is a screening assay. The result is used to decide whether a sample is above or below a threshold. Examples include a preliminary test for a biomarker before a confirmatory test or a high throughput screen for a research cohort. For Tier 1 assays, precision requirements are moderate because the consequence of a small error is a repeat test. Accuracy requirements focus on the threshold region instead of the full range.

Tier 2 is a quantitative research assay. The result is reported as a concentration and used to compare groups, calculate dose response, or support a research conclusion. Examples include measuring a cytokine in a treatment study or quantifying a hormone in a longitudinal cohort. For Tier 2 assays, both precision and accuracy matter across the full reportable range because the numeric value is the primary output.

Tier 3 is a regulated or diagnostic assay. The result is used for patient management, regulatory submission, or a clinical trial endpoint. Examples include a diagnostic marker for a disease or a pharmacodynamic biomarker in a regulated study. For Tier 3 assays, precision and accuracy requirements are the most stringent, and the validation must follow the standards of the relevant regulatory body. The NIST Laboratory Programs provide context on measurement science and quality standards that apply to regulated measurements.

Step 2: Score the Sample Matrix Risk

The sample matrix is the single largest source of accuracy error in an immunoassay. Score the matrix on a three point scale.

Low risk matrices are simple, well defined, and free of interfering substances. Examples include buffer solutions, purified protein preparations, and simple aqueous samples. Low risk matrices rarely cause recovery problems, and a small number of spike recovery experiments is sufficient.

Medium risk matrices are complex but consistent. Examples include serum, plasma, or urine from a defined population. These matrices contain proteins, lipids, and salts that can interfere with binding. Medium risk matrices require spike recovery experiments at multiple concentrations and a check for matrix effects using a dilution linearity experiment.

High risk matrices are complex and variable. Examples include tissue homogenates, whole blood, saliva, or samples from patients with a disease that alters the matrix composition. These matrices can cause severe recovery problems, and the validation must include spike recovery experiments across the full range, dilution linearity, and a comparison to a reference method if one is available.

Step 3: Select the Precision Experiments

Use the assay classification and matrix score to select the precision experiments. The table below shows the minimum recommended experiments for each combination.

Assay Tier	Low Matrix	Medium Matrix	High Matrix
Tier 1 screening	Intra-assay only	Intra-assay and inter-assay	Intra-assay, inter-assay, and inter-laboratory if available
Tier 2 quantitative	Intra-assay and inter-assay	Intra-assay, inter-assay, and inter-laboratory if available	Intra-assay, inter-assay, and inter-laboratory
Tier 3 regulated	Intra-assay, inter-assay, and inter-laboratory	Intra-assay, inter-assay, and inter-laboratory	Intra-assay, inter-assay, and inter-laboratory

The tier matrix combination is a starting point. If the assay is intended to be used for years, invest in the inter-assay experiment early. If the assay is a short term research tool, the intra-assay experiment may be sufficient.

Step 4: Choose the Accuracy Experiments

Accuracy experiments are chosen based on the availability of a reference standard and a reference method.

If a certified reference standard is available, use it to prepare the spike recovery samples. The certified standard provides a known concentration that is traceable to a reference system. The NIST Laboratory Programs describe the role of certified reference materials in measurement science.

If a reference method is available, use it for a comparison study. The reference method should be a method that is known to be accurate, such as a mass spectrometry method or a method that is traceable to a reference standard. The comparison study is more rigorous than spike recovery because it assesses accuracy across a range of concentrations and sample matrices.

If neither a certified standard nor a reference method is available, use a spike recovery experiment with a well characterized in house standard. The in house standard should be prepared from a pure analyte and its concentration should be verified by an independent method if possible.

Step 5: Document the Decision

Write the decision framework into the validation protocol. The protocol should state the assay tier, the matrix score, and the experiments that were chosen. The protocol should also state the reason for the decision. This documentation is essential for reproducibility and for demonstrating that the validation is proportionate to the intended use.

Record System for Precision and Accuracy Data

A validation is only as useful as the records that support it. A structured record system allows you to track precision and accuracy over time, identify trends, and make decisions about revalidation. The record system should be designed before the validation begins and should be used consistently throughout.

The Validation Master Log

The validation master log is a single table that lists every validation experiment. Each row in the log represents one experiment. The columns include the experiment identifier, the date, the operator, the assay lot, the sample matrix, the sample concentration, the number of replicates, the precision result, the accuracy result, and the acceptance criteria. The log is the index to the raw data.

The validation master log should be maintained in a laboratory notebook or an electronic data system. The log should be updated immediately after each experiment is completed. The log should be reviewed by the laboratory supervisor or the quality assurance team at the end of the validation.

The Raw Data Sheet

The raw data sheet is the record of the individual measurements. Each raw data sheet should contain the date and time of the assay, the operator, the reagent lot numbers, the equipment used, the sample preparation, and the raw signal values. The raw signal values are the optical density, the fluorescence, or the counts that are produced by the assay reader.

The raw data sheet should be designed before the validation begins. The sheet should have a defined layout that is easy to follow. The sheet should be filled in during the assay, not after the assay is complete. The sheet should be signed and dated by the operator.

The Calculation Sheet

The calculation sheet is the record of the statistical calculations. The calculation sheet should show the mean, the standard deviation, and the coefficient of variation for each precision experiment. The calculation sheet should show the recovery percentage for each spike recovery experiment and the bias for each reference method comparison.

The calculation sheet should be prepared in a spreadsheet or a statistical software package. The formulas should be visible and should be checked by a second person. The calculation sheet should be stored with the raw data sheet and the validation master log.

The Trend Chart

The trend chart is a graphical record of the precision and accuracy results over time. The trend chart is used to monitor the assay after the validation is complete. The trend chart shows the CV and the recovery for each quality control sample that is run with the routine samples.

The trend chart should be updated after each routine run. The trend chart should be reviewed periodically to identify any trends or changes in the assay performance. A trend toward a higher CV or a lower recovery may indicate that the assay is degrading and needs to be revalidated.

The trend chart is a practical tool for the laboratory. It is the record that shows whether the assay is performing within the acceptance criteria over time. The trend chart is the basis for the decision to continue using the assay or to revalidate it.

Troubleshooting Method for Precision and Accuracy Failures

When a precision or accuracy experiment fails, the cause is often not obvious. A structured troubleshooting method helps you identify the cause and correct it. The method is based on a series of questions that narrow the possible causes.

Step 1: Confirm the Failure

Before you change anything, confirm that the failure is real. Review the raw data sheet and the calculation sheet. Check for transcription errors, calculation errors, and data entry errors. Check that the correct samples were used and that the correct concentrations were recorded. A failure that is caused by a data entry error is not an assay failure.

Step 2: Check the Reagents

Reagents are the most common cause of precision and accuracy failures. Check the reagent lot numbers and the expiration dates. Check the storage conditions of the reagents. Check the preparation of the reagents, including the dilution and the mixing.

If the failure is a high intra-assay CV, the problem may be in the reagent preparation. If the failure is a low spike recovery, the problem may be in the standard preparation. If the failure is an inconsistent inter-assay CV, the problem may be in the reagent lot.

Step 3: Check the Equipment

Equipment is the second most common cause of failure. Check the calibration status of the pipettes, the plate washer, and the reader. Check the temperature of the incubator and the refrigerator. Check the cleanliness of the equipment.

If the failure is a high intra-assay CV, the problem may be in the pipette. If the failure is a low spike recovery, the problem may be in the reader. If the failure is an inconsistent inter-assay CV, the problem may be in the equipment calibration.

Step 4: Check the Operator

The operator is the third most common cause of failure. Check the operator technique, including the pipetting, the mixing, and the timing. Check the operator experience with the assay. Check the operator adherence to the protocol.

If the failure is a high intra-assay CV, the problem may be in the operator pipetting. If the failure is a low spike recovery, the problem may be in the operator sample preparation. If the failure is an inconsistent inter-assay CV, the problem may be in the operator technique.

Step 5: Check the Sample

The sample is the fourth most common cause of failure. Check the sample matrix, the sample preparation, and the sample storage. Check the sample for the presence of interfering substances.

If the failure is a low spike recovery, the problem may be in the sample matrix. If the failure is a high intra-assay CV, the problem may be in the sample preparation. If the failure is an inconsistent inter-assay CV, the problem may be in the sample storage.

Step 6: Document the Troubleshooting

The troubleshooting process should be documented in the validation report. The report should include the failure, the investigation, the cause, and the corrective action. The report should be reviewed by the laboratory supervisor or the quality assurance team.

The troubleshooting record is the basis for the decision to repeat the validation. If the cause is identified and corrected, the validation should be repeated. If the cause is not identified, the validation should be escalated to the quality supervisor or the laboratory director.

Comparison of Validation Approaches

There are two common approaches to immunoassay validation: the fixed protocol approach and the risk based approach. The fixed protocol approach uses a standard set of experiments for every assay. The risk based approach uses the decision framework described above to select the experiments.

The fixed protocol approach is simple and easy to implement. It is appropriate for a laboratory that runs a small number of assays with a similar matrix. The fixed protocol approach is not efficient for a laboratory that runs a wide range of assays with different matrices.

The risk based approach is more efficient and more proportionate. It is appropriate for a laboratory that runs a wide range of assays with different matrices. The risk based approach requires more planning and more documentation.

The choice between the two approaches depends on the laboratory and the intended use of the assay. The risk based approach is more aligned with the NIST Laboratory Programs and the MIQE Guidelines for the reporting of the assay.

Practical Implementation Steps

The following steps are a practical guide to implementing the decision framework and the record system.

Step 1: Write the Decision Framework

Write the decision framework into the validation protocol. The protocol should state the tier, the matrix score, and the experiments that are selected. The protocol should be approved by the laboratory supervisor or the quality assurance team.

Step 2: Create the Record System

Create the validation master log, the raw data sheet, the calculation sheet, and the trend chart. The record system should be created before the validation begins. The record system should be tested with a pilot experiment.

Step 3: Run the Pilot Experiment

Run a pilot experiment to test the record system and the assay. The pilot experiment should be a small version of the full validation. The pilot experiment should be used to identify any problems with the record system or the assay.

Step 4: Run the Full Validation

Run the full validation according to the protocol. The validation should be run in the same manner as the routine samples. The validation should be run with the appropriate controls.

Step 5: Review the Results

Review the results of the validation. The results should be compared to the acceptance criteria. The results should be reviewed by the laboratory supervisor or the quality assurance team.

Step 6: Document the Validation

Document the validation in the validation report. The report should include the protocol, the raw data, the calculations, and the results. The report should be stored in a secure location.

Common Failure Patterns in the Decision Framework

The decision framework can fail in several ways. The following are the common failure patterns.

Over Validation

Over validation is the use of more experiments than are necessary for the intended use. Over validation wastes time and resources. Over validation is common in laboratories that use a fixed protocol for every assay. The risk based approach reduces the risk of over validation.

Under Validation

Under validation is the use of fewer experiments than are necessary for the intended use. Under validation is a risk to the data quality. Under validation is common in laboratories that are under time pressure. The decision framework is designed to prevent under validation.

Misclassification

Misclassification is the assignment of the wrong tier or the wrong matrix score. Misclassification leads to the wrong experiments. Misclassification is common when the intended use of the assay is not clearly defined. The decision framework is designed to prevent misclassification by requiring a written statement of the intended use.

Quality and Safety Context

The decision framework and the record system are part of the quality system of the laboratory. The laboratory should be operated in accordance with the biosafety principles that are described in the Biosafety in Microbiological and Biomedical Laboratories. The laboratory should be operated in accordance with the quality requirements that are described in the NIST Laboratory Programs.

Limitations and Professional Escalation

The decision framework and the record system are limited by the expertise of the laboratory. The framework is a tool for the laboratory, and the laboratory is responsible for the interpretation of the results. The framework is not a substitute for the judgment of the laboratory.

Frequently Asked Questions

What is the difference between precision and accuracy in an immunoassay?

Precision refers to the closeness of repeated measurements of the same sample to each other, while accuracy refers to the closeness of the measured value to the true value. A precise assay can be inaccurate if it consistently produces a value that is different from the true value, and an accurate assay can be imprecise if the measurements vary widely around the true value.

How many replicates are needed for a precision assessment?

The number of replicates depends on the assay and the expected variability. A common approach is to use at least 6 replicates for the intra-assay precision and at least 3 runs for the inter-assay precision. The exact number should be defined in the validation protocol and should be sufficient to provide a reliable estimate of the variability.

What is an acceptable coefficient of variation for an immunoassay?

The acceptable coefficient of variation depends on the application and the analyte concentration. For research assays, a CV below 15% is often considered acceptable, but the threshold should be defined in the validation protocol. The acceptance criteria should be based on the intended use of the assay and the variability that is acceptable for the application.

How is spike recovery used to assess accuracy?

Spike recovery is performed by adding a known amount of the analyte to a sample and measuring the recovery. The recovery is calculated as the measured concentration divided by the expected concentration, multiplied by 100. A recovery of 100% indicates that the assay is accurate, while a recovery below or above 100% indicates a bias.

What is the role of a reference method in accuracy assessment?

A reference method is a method that is known to be accurate and is used to compare the results of the assay. The assay is used to measure a set of samples, and the results are compared to the results obtained with the reference method. The bias is calculated as the difference between the measured value and the reference value, expressed as a percentage of the reference value.

How often should an immunoassay be revalidated?

An immunoassay should be revalidated when there is a change in the assay, such as a change in the reagents, the equipment, or the operator. The assay should also be revalidated when the assay is used for a new application or a new sample matrix. The revalidation should be performed in accordance with the protocol and the acceptance criteria.

What are the common causes of high intra-assay variability?

High intra-assay variability can be caused by the assay, the reagents, the equipment, or the sample. The variability can be reduced by using the same reagents, calibrating the equipment, using the same operator, and controlling the sample and the time. The variability should be investigated to identify the cause and to reduce the variability.

What should be done if the validation results are not within the acceptance criteria?

If the validation results are not within the acceptance criteria, the validation should be investigated to identify the cause. The investigation should include the review of the protocol, the raw data, and the calculations. The corrective action should be determined, and the validation should be repeated.

Related Molecular Biology Guides

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.
Interference in ELISA.. Methods in molecular biology (Clifton, N.J.), 2023.
Introduction to qualification and validation of an immunoassay.. Pharmaceutical statistics, 2025.
Transforming kidney transplant monitoring with urine CXCL9 and CXCL10: practical clinical implementation.. Scientific reports, 2024.
A Multicenter Validation of a Fully Automated Chemiluminescence Immunoassay for Plasma Elafin in Psoriasis Diagnosis and Severity Assessment.. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025.
Important Considerations for ELISpot Validation.. Methods in molecular biology (Clifton, N.J.), 2024.

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