Method Validation Edition

Method validation software used in regulatory submissions worldwide Method comparison, precision, linearity, detection limits and reference intervals in one package. It carries the statistics and plots the CLSI EP protocols call for, and the ones laboratories used before them, for IVD manufacturers, clinical laboratories and researchers.

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Every feature from all five editions for 15 days.
Method Validation edition from US$ 475 a year · 30-day money-back guarantee.

Microsoft Excel with the Analyse-it tab of the Method Validation edition selected: a method comparison report with the scatter plot, the ordinary least squares fit with its confidence interval and the allowable difference bands, the Method Comparison task pane open on the fit options, and the Method Comparison menu dropped open on the ribbon listing the plots, bias estimators and agreement estimators. Handwritten notes: Runs inside Excel, and every edition includes the statistics research needs:; Method Validation adds method comparison, MSA - trueness, precision, linearity - diagnostic accuracy and reference intervals; Linear fit with its 95% CI and the allowable difference: plots and tables, on a worksheet; Every option for the analysis is set here, then Recalculate; The output is an Excel worksheet: share it with colleagues, auditors or regulators, archive it, open it on any PC with Excel.

Trusted by 75,000 scientists at thousands of laboratories and 8 of the top 10 IVD manufacturers for nearly 30 years.

Abbott Roche Siemens Healthineers Beckman Coulter Thermo Fisher Sysmex bioMérieux Becton Dickinson
We use Analyse-it for the analysis of data necessary to file 510k. We chose Analyse-it because it works in Excel, includes CLSI protocols, and, unlike EP-Evaluator, lets us analyze data directly from equipment without typing.
Thomas D Harrigan, Ph.D.
Technical Product Manager
Alfa Wassermann Diagnostic Technologies

Analyse-it is developed for and is in use at thousands of laboratories and IVD manufacturers. Among them are ISO/IEC 17025 accredited testing and calibration laboratories, ISO 15189 accredited medical laboratories and CLIA ’88 regulated medical laboratories.

Laboratories and manufacturers use Analyse-it for development, support, product labelling and FDA 510(k) submissions.

Establish precision, linearity, bias and detection capability during product development. Compare your method against a predicate or reference procedure. Establish reference intervals and diagnostic accuracy for product labelling.

Verify manufacturer claims when bringing a new analyser or reagent system into clinical use. Produce the statistical evidence for a 510(k) submission, CE-IVD technical file, CAP inspection or ISO 15189 audit.

CLSI EP protocols covered — and the statistics behind them since 1997

Analyse-it has been a leader in method validation statistics software for nearly 30 years. Many of the procedures now codified in CLSI guidelines were first made widely accessible through Analyse-it, and their adoption into formal guidelines followed. Passing-Bablok regression, Weighted Deming regression and non-parametric reference interval methods are among them. The analyses are not scripted protocol wizards but the statistics and plots themselves, which you configure and combine as your study requires. The same analyses serve a regulatory submission, or method comparison, agreement and reference interval work with no submission behind it.

EP05-A3 Evaluation of Precision of Quantitative Measurement Procedures Precision
EP06-Ed2 Evaluation of the Linearity of Quantitative Measurement Procedures Linearity
EP09-A3 Measurement Procedure Comparison and Bias Estimation Using Patient Samples Method comparison
EP10-A3-AMD Preliminary Evaluation of Quantitative Clinical Laboratory Measurement Procedures MSA
EP12-A2 User Protocol for Evaluation of Qualitative Test Performance Diagnostic performance
EP14-A3 Evaluation of Commutability of Processed Samples Method comparison
EP15-A3 User Verification of Precision and Estimation of Bias MSA
EP17-A2 Evaluation of Detection Capability for Clinical Laboratory Measurement Procedures Detection capability
EP21-A Estimation of Total Analytical Error for Clinical Laboratory Methods Method comparison
EP24-A2 Assessment of the Diagnostic Accuracy of Laboratory Tests Using Receiver Operating Characteristic Curves Diagnostic performance
EP28-A3c Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory Reference intervals

Characterise measurement system performance

Establish the precision, trueness, linearity and detection capability of a measurement procedure. You may be an IVD manufacturer characterising performance during development for a 510(k) submission, or a clinical laboratory verifying manufacturer claims:

  • Precision with flexible nested variance component designs — repeatability, between-run, between-day, between-operator, between-site, within-laboratory and reproducibility (EP05-A3)
  • Precision profiles — SD or CV against concentration with seven variance function models, including the 3-parameter power and 4-parameter turning-point functions. Inverse prediction gives the concentration at a chosen CV
  • Linearity with polynomial regression, Hsieh-Liu nonlinearity testing and adjustable measuring interval for reportable range determination (EP06-Ed2)
  • Detection capability — LoB, LoD and LoQ using parametric, non-parametric, probit regression and variance function approaches (EP17-A2)
  • Verification of precision and trueness against manufacturer claims — such as testing observed CV against a claimed 3.5% — with χ² and equivalence tests (EP15-A3)
  • Preliminary evaluation — precision and bias at three levels against allowable limits, before the full studies (EP10-A3-AMD)
Measurement system analysis details →
Microsoft Excel showing the precision profile of the CA19-9 precision report: CV % against concentration on a logarithmic axis for repeatability, within-laboratory precision and reproducibility, each with its fitted 3-parameter variance function, and the model, equation and parameter table beneath, with the Precision task pane open. Handwritten notes: Precision profile: CV against concentration, for each level of the design; The fitted variance function and its parameters; Variance function: seven models.
The CA19-9 precision profile: CV against concentration for repeatability, within-laboratory precision and reproducibility, with the fitted 3-parameter variance function and its parameters for each.

Compare methods and estimate bias

When introducing a new measurement procedure, replacing an existing one or comparing against a reference method, you need to know the bias and whether it affects clinical decisions:

  • Passing-Bablok regression (1983 and 1988) — non-parametric, robust to outliers, with CUSUM linearity test and bootstrap CIs
  • Deming and Weighted Deming regression — accounts for measurement error in both methods, systematic error decomposition into constant and proportional bias, Syx independent precision estimate
  • Bland-Altman limits of agreement — mean and median bias, constant and non-constant precision, linear fit for concentration-dependent bias, allowable difference band
  • Bias at specified clinical decision points with confidence intervals, equality and equivalence tests. Allowable difference as absolute, percentage or combination — such as “10%, with a minimum of 5 mg/dL”
  • Total analytical error per EP21-A — bias and imprecision combined and judged against the allowable total error, with the mountain plot of the differences
  • Partition the measuring range into separate intervals, each with its own fit and allowable difference specification
  • Commutability per EP14-A3 — processed samples and reference materials compared against patient samples, with prediction intervals showing which fall outside
  • Qualitative methods — positive and negative agreement, and kappa and weighted kappa with linear or quadratic weights, for two methods with no known true state (EP12-A2)
Method comparison details →
Microsoft Excel showing a method comparison report for LDL cholesterol: the Bland-Altman difference plot with the 95% limits of agreement and the allowable difference band, with the task pane open on Fit Differences. Handwritten notes: Difference plot: Y - X against X; 95% limits of agreement and the allowable difference band; Bias model: mean, median or linear fit; limits.
LDL cholesterol, 100 samples, from EP21-A Table 2: the difference plot with the 95% limits of agreement and the ±10 mg/dL allowable difference band.

Establish and verify reference intervals

Reference intervals are essential for clinicians to interpret results and make a diagnosis. Analyse-it covers the full EP28-A3c workflow. Choose the right quantile method for your data and sample size, screen for outliers and partition, then transfer and verify against a new procedure or population.

  • Five quantile methods — parametric, non-parametric (three computation approaches), robust bi-weight, bootstrap and Harrell-Davis
  • Partition by the factors that affect the intervals — such as sex and age group for alkaline phosphatase — with one analysis per partition and the descriptive statistics for each
  • Full range of transformations — log, square root, cube root, Box-Cox, Manly exponential, two-stage exponential/modulus
  • Outliers with Tukey box plots, and normality assessment with Shapiro-Wilk, Anderson-Darling and frequency histogram
  • Transfer and verify intervals — a published interval checked against 20 samples per partition with the binomial proportion test, as EP28-A3c describes, or transferred by regression
Reference interval details →
Microsoft Excel showing a reference interval report for ALT in females: the raw histogram with its reference limits, and beneath it the Transformed histogram of ln ALT with a normal curve overlaid, with the task pane open on the Transform section. Handwritten notes: Raw ALT: skewed, with its reference limits; The same data after a log transform, normal curve overlaid; Transform: log, Box-Cox, Manly or two-stage modulus.
ALT in females: the raw histogram with its reference limits, and beneath it the same data after a natural log transformation with the normal curve overlaid.

Evaluate diagnostic test accuracy

Establish and compare the ability of a diagnostic test to discriminate between outcomes. Explore how the test differentiates between positive and negative cases and determine optimum decision thresholds that allow for the clinical costs of misdiagnosis:

  • Empirical ROC curves per EP24-A2, with the area under the curve, its DeLong confidence interval and the test against no discrimination
  • AUC comparison (DeLong) for up to 10 paired or independent tests — with equality, equivalence and non-inferiority options
  • Sensitivity, specificity, likelihood ratios, predictive values at a prior probability, and the diagnostic odds ratio — each with its confidence interval
  • Optimal decision threshold by Youden’s index, the point closest to (0,1) or the clinical costs of misdiagnosis — and the sensitivity and specificity at any threshold
  • Decision plots of sensitivity and specificity, likelihood ratios, predictive values or cost across all possible thresholds
  • Qualitative test evaluation — sensitivity and specificity with confidence intervals, and the difference between two tests (EP12-A2)
Diagnostic performance details →
Microsoft Excel showing the ROC Curve section of a diagnostic performance report: ROC curves for OxLDL and LDL, and the AUC table with DeLong 95% confidence intervals, SE, Z statistic and p-value for each test, with the task pane open on ROC Curve. Handwritten notes: ROC curve for each test; AUC with DeLong 95% CI, Z test and p-value; AUC estimator, CI and method.
ROC curves for OxLDL and LDL on one chart, and the AUC of each test with its DeLong 95% confidence interval, standard error, Z statistic and p-value.
I started working with Analyse-it about 15 years ago. I was then working for a medical laboratory in method evaluation and ISO certification. The certification expert was delighted that I use the same statistical tool that he uses. Later in the pharma industry I applied it again to method evaluation and statistical work to analyse our projects. Now I have a company for clinical trials. Even tasks with ten thousands of numbers are no problem with Analyse-it.
Peter J.
Clinical Immunology FAMH

Includes the full Standard edition

All features from the Standard edition are included — ANOVA, PCA, regression, correlation, distribution analysis, hypothesis tests and more.

When a validation study raises a question — an unexpected distribution, a suspect outlier, a relationship worth examining — the full general statistics toolkit is in the same workbook.

Related guides in the Learn section: which CLSI EP protocol do you need, validation versus verification, choosing a regression for method comparison and what CLIA requires before you report a result.



Software you can trust

Validated calculations you can defend at inspection Every calculation is performed by Analyse-it — no Excel formulas, no third-party functions. Results are validated against CLSI reference datasets, published datasets and thousands of internal test cases before every release. How Analyse-it is developed and validated →
Data stays in your facility Analyse-it runs entirely within Microsoft Excel on your PC. No cloud processing, no data transmission. Pre-submission data, data derived from patients and in-process results stay within your facility under your own data governance controls.
Standard Excel workbooks anyone can open Every analysis is an ordinary .xlsx workbook. Share with colleagues, submit to regulatory affairs, archive for audit, open on any PC with Excel. No proprietary format, no licence required to view results. Colleagues and auditors see exactly what you see.
Results that cannot be accidentally broken Analysis output contains computed values, not formulas. Nothing to accidentally overwrite, no cell references to break, no formula errors to introduce. The results you reported are exactly what you will find when you reopen the workbook months or years later for an audit.

Customer stories

How clinical laboratories and IVD manufacturers use Analyse-it for method validation in regulated environments.

Method validation in half the time at a Swiss laboratory

Ente Ospedaliero Cantonale — the regional clinical chemistry laboratory for Ticino, Switzerland — needed to compare performance across 110–115 parameters when transitioning to new analysers. The laboratory used Analyse-it across the CLSI EP05, EP09, EP15 and EP28 protocols and completed the project in half the expected time.

Read the EOC case study →

Streamlined CLSI method validation at a US reference laboratory

A research and development scientist at a US National Reference Laboratory replaced custom Excel spreadsheets and multiple validation tools with Analyse-it. One application gave access to Bland-Altman, Passing-Bablok, Deming and Weighted Deming regression, and saved roughly a day per validation study.

Read the National Reference Laboratory case study →

Example analyses

Download CLSI example datasets, open them in the trial and see exactly what the output looks like.

EP09 A3 Example 1 2 pages EP09-A3 — Appendix I
Difference plots over a partitioned interval. 79 observations split at 1.8 µg/L, 40 below and 39 above. Each interval gets its own difference plot, mean difference with a 95% CI and a hypothesis test. The allowable difference is ±0.06 µg/L below the split and ±6% above it. Neither interval shows a significant bias.
EP09 A3 Example 2 10 pages EP09-A3 — Appendix I
All five regression fits. 79 observations fitted five ways — ordinary least squares, weighted least squares, Deming, weighted Deming and Passing-Bablok. Each analysis gives the equation, parameter estimates with CIs and the predicted bias at a medical decision point of 5 µg/L. Weighted least squares is the fit that misses the allowable difference.
EP05 A3 Example 1 1 page EP05-A2 — Appendix B
Glucose precision, single site. 20 days × 2 runs × 2 observations, 80 results. Variability plot, then repeatability, between-run, within-day, between-day and within-laboratory components with CIs and the ANOVA table with expected mean squares.
EP05 A3 Example 2 3 pages EP05-A3 — Appendix B
CA19-9 precision, multi-site. 6 samples × 3 laboratories × 5 runs × 5 observations, 450 results. Repeatability, within-laboratory and reproducibility for each sample, then a precision profile fitted with a three-parameter variance function.
EP15 A3 Example 1 3 pages EP15-A3 — Table 8
Ferritin precision verification. 3 samples × 5 runs × 5 observations. Outlier identification with the flagged results excluded, then a variability plot and within-run and total precision. A χ² test of each against the manufacturer’s claim at a 1.67% individual significance level.
EP06 A Example 1 3 pages EP06-A — Appendix C
IgM linearity. 5 dilutions × 2 observations. Linear, second and third order polynomial fits, then nonlinearity at each dilution with 95% CIs against a ±5% allowable band, and a difference plot. Only dilution 4 meets the requirement.
EP17 A2 Example 1 2 pages EP17-A2 — Appendix A
Estradiol detection capability, two reagents. 5 blank and 5 low-level samples per reagent, 60 results each. Within-run and total precision for every sample, the limit of blank from the blank distribution and the limit of detection from the pooled SD of the low-level samples. A bi-histogram sets the blank against the low-level material.
EP28 A3 Example 1 2 pages EP28-A3c — Table 4
Calcium reference intervals by sex. 120 observations per sex, reported separately. Distribution with descriptive statistics, and nonparametric reference limits from the (N+1)p quantile with 90% CIs.
EP24 A2 Example 1 2 pages EP24-A2 — Appendix D
OxLDL and LDL diagnostic accuracy. 50 subjects, 28 of them with the condition. ROC curves for both markers with AUC, CIs and a test against 0.5 — OxLDL 0.80, LDL 0.56 — and a DeLong comparison of the two curves. A second analysis adds the bi-histogram and decision threshold plot for OxLDL alone.
EP12 A2 Example 1 2 pages EP12-A2 — Example 10.3.1
H. pylori, two qualitative tests against a known state. 102 subjects. Mosaic plots, sensitivity and specificity with Wilson 95% CIs and predictive values at the observed prior. The difference between the two tests with Newcombe CIs and a score Z test.

Free trial and pricing

Try it on your own data first. The 15-day trial is every feature from all five editions — install it and start straight away.

Download 15-day trial See pricing or get a quote →

Method Validation edition: US$ 475 per year or US$ 1155 for a perpetual licence. Every purchase carries a 30-day money-back guarantee. Need a quote for purchasing? Choose the licence on the pricing page and click Get a quote.

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Technical details

Method comparison

CLSI protocols

  • EP09-A3: Measurement Procedure Comparison and Bias Estimation Using Patient Samples
  • EP14-A3: Evaluation of Commutability of Processed Samples
  • EP21-A: Estimation of Total Analytical Error for Clinical Laboratory Methods

Quantitative methods (EP09-A3)

  • Singlicate, duplicate and replicate measurements
  • Reduce measuring interval to linear range, or partition into multiple intervals with different relationships
  • Ordinary and Weighted linear regression average bias with confidence intervals
  • Deming and Weighted Deming regression average bias with jackknife confidence intervals
  • Passing-Bablok regression (1983 and 1988) average bias with Passing-Bablok or bootstrap confidence intervals
  • Bland-Altman limits of agreement with mean, median and linear fit bias
  • Limits of agreement — constant and non-constant precision
  • Confidence intervals on limits of agreement
  • Non-parametric (percentile) limits of agreement
  • Limits of agreement for single measurements or for the mean of replicates
  • Within-subject variance estimation from replicates
  • Predict bias with confidence intervals at important decision levels
  • Test equality (no difference) or equivalence (difference within allowable difference) at decision levels
  • Compare commutability of samples with prediction intervals
  • Precision (SD or CV) and precision plots for each method
  • Pearson r correlation coefficient

Total analytical error (EP21-A)

  • Combine bias from method comparison with imprecision of test method
  • Assess against allowable total error
  • Allowable error as absolute, percentage or combination

Qualitative methods

  • Proportion in positive/negative agreement (Clopper-Pearson exact, Wilson score CIs)
  • Kappa and Weighted Kappa with linear or quadratic weights (Wald Z CI)
  • Kappa test for agreement
  • Bangdiwala agreement plot new in v5.60
  • Dice-Sørensen average agreement new in v5.60

Plots

  • Scatter plot with average bias, confidence bands, identity line and allowable difference band
  • Scatter plot with allowable error bands
  • Vary colour of points by a factor
  • Difference / relative difference / ratio plot against X or mean of methods with allowable difference band and histogram
  • Linear or log X-axis on difference plots new in v5.51
  • Mountain plot with allowable difference band
  • Residual plot (raw and standardised) with histogram
  • CUSUM linearity plot and Kolmogorov-Smirnov linearity test

Measurement system analysis

CLSI protocols

  • EP05-A3: Evaluation of Precision of Quantitative Measurement Procedures
  • EP06-Ed2: Evaluation of Linearity of Quantitative Measurement Procedures
  • EP10-A3-AMD: Preliminary Evaluation of Quantitative Clinical Laboratory Measurement Procedures
  • EP15-A3: User Verification of Precision and Estimation of Bias
  • EP17-A2: Evaluation of Detection Capability for Clinical Laboratory Measurement Procedures

Experiment design

  • Flexible balanced and unbalanced experiment design: up to 3 random nested factors and 1 fixed factor
  • Generalised ESD outlier identification

Precision (EP05-A3)

  • Precision as variance, SD or CV% with exact, Satterthwaite and MLS confidence intervals
  • Abbreviated reproducibility/repeatability, and detailed intermediate precision components
  • ANOVA table
  • χ² test against precision claim
  • Precision profile of SD or CV
  • Variance function fit: constant variance, constant CV, mixed constant/proportional, 2-parameter, Sadler 3-parameter power and 3-parameter alternative power
  • 4-parameter variance function with turning point
  • Predict the SD or CV at any concentration from the variance function new in v5.51

Bias / trueness (EP15-A3, EP10-A3-AMD)

  • Bias with confidence interval
  • Test equality (no bias) or equivalence (bias within allowable bias)

Linearity (EP06-Ed2) new in v5.81

  • Linear, polynomial (up to 5th order), forward stepwise polynomial and best polynomial regression
  • Weighted fits for non-constant SD: by 1/X, variance, pooled variance or a variance function
  • Adjust measuring interval to find linear range
  • Assigned values: known, or relative by mixture, addition or dilution series
  • Difference between linear and nonlinear fit with Hsieh-Liu confidence intervals
  • Test equality or equivalence against allowable nonlinearity
  • Emancipator-Kroll linearity

Detection capability (EP17-A2)

  • LoB: parametric (SD of blank material), non-parametric (quantile of blank material) or from precision profile variance function
  • LoD: pooled SD of non-blank materials, from precision profile variance function or probit regression
  • LoQ: from the precision profile variance function against an allowable total error (Westgard, RMS, precision-only or bias plus precision)

Plots

  • Scatter and difference plots
  • Variability of measurements plot
  • Precision profile with variance function
  • Difference plot of bias against assigned values with allowable bias bands
  • Linearity plot with polynomial fits
  • Difference plot with allowable nonlinearity band
  • Frequency density histogram with LoB and LoD
  • Probit regression curve
  • Multiple inverse predictions with confidence intervals new in v5.65

Reference intervals

CLSI protocols

  • EP28-A3c: Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory

Establish reference limits

  • Normal (parametric) quantile, Z (MVUE) or t-based
  • Non-parametric quantile: (N+1)p, Np+½, (N+⅓)p+⅓
  • Harrell-Davis quantile
  • Bootstrap quantile
  • Robust bi-weight quantile
  • Confidence intervals on all reference limits
  • Bootstrap confidence intervals
  • Two-sided reference interval or one-sided reference limit

Transfer / verify

  • Transfer using method comparison regression function
  • Binomial test for proportion inside reference interval

Partitioning & transformations

  • Partition by factor(s)
  • Reciprocal transformation
  • Log transformation
  • Square and cube root
  • Box-Cox
  • Manly exponential
  • 2-stage exponential / modulus

Normality testing

  • Shapiro-Wilk test
  • Anderson-Darling test
  • Normal Q-Q plot

Plots

  • Frequency distribution histogram with normal overlay and reference limits
  • Tukey outlier box plot
  • Normal Q-Q plot with Lilliefors confidence band

Diagnostic performance

CLSI protocols

  • EP24-A2: Assessment of the Diagnostic Accuracy of Laboratory Tests Using Receiver Operating Characteristic Curves
  • EP12-A2: User Protocol for Evaluation of Qualitative Test Performance

ROC analysis (EP24-A2)

  • 1 test, up to 10 paired tests or up to 10 independent tests/groups
  • Empirical (non-parametric) ROC curves
  • ROC curve plot with the no-discrimination line
  • Overlaid ROC curves for test comparison
  • Wilcoxon-Mann-Whitney AUC with DeLong-DeLong-Clarke-Pearson CI
  • Z test that the AUC exceeds 0.5 (no discrimination)
  • Compare DeLong AUC difference — equality, equivalence or non-inferiority
  • Comparisons for all pairs of tests or each against a control
  • Number of TP, TN, FP, FN
  • Sensitivity, specificity with Clopper-Pearson exact or Wilson score CI
  • Positive and negative likelihood ratios
  • Positive and negative predictive values
  • Predictive values at multiple prior probabilities (prevalences) new in v5.51
  • Diagnostic odds ratio and Youden index
  • Optimal threshold: Youden, closest-to-(0,1), cost-based
  • Decision plot: sensitivity/specificity, likelihood ratios, predictive values or cost vs threshold
  • Bi-histogram and dot plot
  • Predict FPF at fixed sensitivity, sensitivity at fixed FPF or sensitivity/FPF at fixed threshold

Qualitative tests (EP12-A2)

  • 1 test, 2 paired tests or 2 independent tests/groups
  • Sensitivity, specificity with Clopper-Pearson exact or Wilson score CI
  • Positive and negative likelihood ratios with Miettinen-Nurminen score CI
  • Predictive values at the observed or a specified prevalence, with Mercaldo-Wald logit CI
  • Diagnostic odds ratio and Youden index
  • Mosaic plot of outcomes
  • Difference between sensitivity/specificity with Newcombe score CI, plus Tango score CI for paired tests and Miettinen-Nurminen score CI for independent tests
  • Equivalence and non-inferiority tests for sensitivity/specificity new in v5.65
  • McNemar-Mosteller exact, Fisher exact and score Z test

System requirements

  • Excel 365, 2024, 2021, 2019 or 2016 for Windows (32- and 64-bit)
  • Windows 11 or 10, or Windows Server 2016 onwards
  • 2 GB RAM minimum recommended
  • 80 MB disk space