Analyse-it Method Validation Edition Validate and verify your analytical and diagnostic methods to meet the demands of regulatory compliance.

The leading software package for method validation for over 20-years.

Analyse-it is developed for and is in use at thousands of ISO/IEC 17025 accredited testing and calibration laboratories, ISO 15189 accredited medical laboratories, CLIA '88 regulated medical laboratories, and IVD manufacturers for development, support, product labeling and FDA 510(k) submissions.


  • We use Analyse-it frequently for our verification and pre-verification work, in accordance with CLSI guidelines for in-vitro diagnostics. It's saved time and effort compared to the hodge-podge of applications we used before, JMP, SAS, etc...
    Brian Noland, Ph.D.
    Principle Scientist, Product Development
    Biosite / Inverness Medical Innovations
  • 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
  • I used Analyse-It for many product development, product troubleshooting, and technology evaluation activities... your product was the easiest to use, was accurate, and produced publication ready reports.
    Stanley F. Cernosek, Ph.D.
    Clinical Chemistry Reagent Development
    Beckman Coulter, Inc.
  • Analyse-it has been a tremendous help. I've published and presented at national cardiology meetings and couldn't have accomplished most of my research without it. Using Analyse-it, I even found errors or omissions in the work of our statistician!
    Regina S. Druz, MD, FACC, FASNC
    Director, Nuclear Cardiology
    North Shore University Hospital

Built for CLSI protocols

The latest Clinical and Laboratory Standards Institute (CLSI) method validation protocols are recognized by the College of American Pathologists (CAP), The Joint Commission, and the US Food and Drug Administration (FDA).

That's why we included extensive support for 11 CLSI protocols

 EP05-A3
Evaluation of Precision of Quantitative Measurement Procedures
 EP15-A3
User Verification of Precision and Estimation of Bias
 EP06-A
Evaluation of the Linearity of Quantitative Measurement Procedures
 EP17-A2
Evaluation of Detection Capability for Clinical Laboratory Measurement Procedures
 EP09-A3
Measurement Procedure Comparison and Bias Estimation Using Patient Samples
 EP21-A
Estimation of Total Analytical Error for Clinical Laboratory Methods
 EP10-A3-AMD
Preliminary Evaluation of Quantitative Clinical Laboratory Measurement Procedures
 EP24-A2 (Replaces GP10-A)
Assessment of the Diagnostic Accuracy of Laboratory Tests Using Receiver Operating Characteristic Curves
 EP12-A2
User Protocol for Evaluation of Qualitative Test Performance
 EP28-A3C (Formerly C28-A3C)
Defining, Establishing, and Verifying Reference Intervals in the Clinical Laboratory

Validate and verify measurement system performance characteristics

It’s essential to ensure the performance characteristics (precision, trueness, linearity, interferences, detection capability) of a measurement procedure meet the requirements for intended use. Manufacturers (IVD companies) must establish performance during product development to feedback into the development process, for FDA 510k submissions and product marketing, and to support customers in the field. Laboratories must verify they can achieve the manufacturer's claimed performance during implementation of a new measurement system, during regulatory inspections (under the CLIA ’88 act), and as part of proficiency testing (PT) schemes. Measurement systems analysis (MSA) lets you determine all these important performance characteristics in one analysis.

Examine diagnostic test performance to find the most effective

Rated best ROC curve software in Clinical Chemistry March 2003 vol. 49 no. 3 pg. 433-439, Analyse-it lets you establish and compare the ability of a diagnostic test to correctly diagnose patients. Explore how the test differentiates between positive and negative cases and explore optimum decision thresholds factoring in the costs of misdiagnosis.

Compare methods and evaluate the impact of making changes

When introducing a new measurement procedure you want to see how it stacks-up against your existing procedure or evaluate its performance against the gold-standard. Bland-Altman lets you see the agreement between methods and what effect the differences between methods might have on clinical interpretation. More advanced procedures like Deming regression and Passing-Bablok tell you the bias between methods, how medical decision points may be affected, and let you test if bias meets performance requirements.

Establish reference intervals to make clinical diagnoses

Reference intervals are essential for clinicians to interpret results and make a diagnosis. As a laboratory it's your job to provide normal reference ranges they can rely on. With the widest range of methods available in any software package, the ability to partition the intervals by factors such as sex, age, ethnicity, Analyse-it makes it easy to establish reference ranges or transfer them to a new measurement procedure.

Find out more...

Plus it includes the
Analyse-it Standard Edition...

All the features from the Analyse-it Standard Edition are included so you can improve processes and products by identifying solutions using hypothesis tests and model fitting techniques.

Integrated into Microsoft Excel, so it's easy to use...

That's right. Analyse-it integrates into Microsoft Excel 2007, 2010, 2013, and 2016 for Microsoft Windows. There's virtually no learning curve, and the intuitive user interface and logical task-based workflow makes sense to those of us that aren’t programmers or full-time statisticians.

All your data and results are kept in Excel workbooks, making it easy to collaborate and share them with colleagues, and meaning there's no locked-in file format.

Software you can trust

We've developed software for method validation for more than 25 years, and popularized statistical procedures that have since been adopted into method validation standards and guidelines.

But it's our customers that matter - the independent laboratories, regulatory agencies, and many of the world's top ten IVD companies. Here are just a few of them...

Case study

Analyse-it® Reduces Method Validation steps at National Reference Laboratory

With Analyse-it, I pull in the data and quickly analyse it, and then prepare figures for manuscripts right there. From the beginning of the project to completion, using one application saves me probably a day's worth of time.

Case study

Analyse-it® Cuts Project Time in Half at Swiss Laboratory

Analyse-it has a tremendous advantage in its ease of use. With other programs, you really have to study how to use them, but Analyse-it makes it so easy, and at the same time offers the advanced procedures we need like Weighted Deming regression.

Accurate and reliable

You might have heard Microsoft Excel isn't up to the job of statistical analysis. It isn't. The built-in functions just aren't built for accuracy. So we don't use a single one. Instead, Analyse-it handles all of the calculations internally, using reliable algorithms and IEEE 754 double floating point precision. And the results to prove it? See Analyse-it's performance against the NIST-StRD.

Validated, tested at every stage

We've conducted thousands of tests to put Analyse-it through its paces. They cover all releases and service packs of Microsoft Excel 2007, 2010, 2013 and 2016. What's more, validation tests are run automatically after every change to the software so you can be confident the statistics are correct. And stay correct. See our development & validation process.

Dependable support

No need to resort to old textbooks. Analyse-it includes detailed help that’s written in plain English. And if you get stuck we’re here for extra help - aiming to answer your queries within 24 hours.

Technical specification

System requirements
  • Microsoft Excel 2007, 2010, 2013 & 2016 (32- and 64-bit)
  • Microsoft Windows XP, Vista, 7, 8, 10, Server 2003, 2008, 2012, & 2016
  • 2GB RAM minimum recommended
  • 50MB disk space
Method comparison
Quantitative methods
  • Supports singlicate, duplicate, and replicate measurements.
  • Reduce measuring interval to linear range, or partition into multiple intervals with different relationships (e.g. constant / relative differences) new in v4.00
  • Ordinary and Weighted linear regression average bias with confidence intervals
  • Deming and Weighted Deming regression average bias with Jacknife confidence intervals
  • Passing Bablok regression average bias with Passing-Bablok or Bootstrap confidence
  • Predict bias with confidence intervals at important decision levels
  • Test equality (no difference) or equivalence (difference within an allowable difference) at decision levels
  • Scatter plot, with average bias, average bias confidence bands, identity line, and allowable difference band
  • Vary color of points by a factor
  • Difference/relative difference/ratio plot against X or mean of methods with allowable difference band and histogram of differences
  • Bland-Altman limits of agreement with mean, median, and linear fit bias new in v3.75
  • Mountain plot with allowable difference band new in v3.71
  • Residual plot, raw and standardized, with histogram of residuals
  • CUSUM linearity plot and Kolmogorov-Smirnov linearity test
  • Precision (SD or CV) and precision plots for each method
  • Pearson r correlation coefficient
  • Supports CLSI EP09-A3 Measurement Procedure Comparison and Bias Estimation Using Patient Samples and CLSI EP21 Estimation of Total Analytical Error for Clinical Laboratory Methods
Qualitative methods
  • Proportion in positive agreement / negative agreement with Clopper-Pearson exact or Wilson score confidence intervals
  • Kappa and Weighted Kappa for chance-corrected agreement with Wald Z confidence interval
  • Kappa test for agreement
Measurement Systems Analysis (MSA)
  • Unified analysis to examine performance characteristics of a method (bias, precision, linearity, interferences, and detection limits)
  • Flexible balanced and unbalanced experiment design: up to 3 random nested factors new in 4.60 (e.g. day, run, laboratory), and 1 fixed factor (e.g. level)
  • Scatter plot and difference plots
  • Variability of measurements plot
Bias / Trueness new in v4.00
Linearity
Precision
Detection limits new in v4.00
  • Limit of blank (LoB): of a blank material (parametric SD or non-parametric quantile), or using precision profile variance function
  • Limit of detection (LoD): pooled SD of non-blank materials, or using precision profile variance function
  • Limit of quantitation (LoQ) using precision profile variance function
  • Frequency density histogram of detection capability, LoB, and LoD
  • Supports CLSI EP17-A2 Evaluation of Detection Capability for Clinical Laboratory Measurement Procedures
Reference interval
Establish reference limits
  • Normal quantile
  • Nonparametric quantile, Harrell-Davis quantile, and Bootstrap quantile
  • Robust bi-weight quantile for symmetric and skewed small samples
  • Various quantile computation methods (N+1)p, Np+1/2, and (N+1/3)p+1/3
Transfer / verify limits
  • Transform existing reference interval using method comparison regression function
  • Binomial test for proportion inside reference interval
Diagnostic performance
ROC
  • Diagnostic performance for 1 test, up to 10 paired tests, or up to 10 independent tests/groups
  • Number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN)
  • Sensitivity/Specificity
  • Likelihood ratios
  • Predictive values
  • Odds ratio, and Youden's index
  • Bi-histogram and dot-plot of positive/negative outcomes new in v4.0
  • ROC curve plot: Sensitivity (TPF) vs 1-Specificity (FPF) with no discrimination line
  • Wilcoxon-Mann-Whitney area under curve with DeLong-DeLong-Clarke-Pearson confidence interval
  • Z test of area under curve is better than chance decision
  • Compare DeLong-DeLong-Clarke-Pearson difference in area under curves and test for equality (no difference), equivalence (difference negligible), or non-inferiority (not unacceptably worse than a standard test)
  • Decision plot of accuracy over all possible decision thresholds: Sensitivity vs Specificity, Likelihood ratios, Predictive values, or Cost
  • Find optimal decision threshold based on cost of diagnosis/misdiagnosis
  • Predict false positive fraction (FPF) at: Fixed sensitivity, Sensitivity at fixed FPF, or Sensitivity/FPF at fixed threshold.
  • Supports CLSI EP24-A2 (Replaces GP10-A) Assessment of the Diagnostic Accuracy of Laboratory Tests Using Receiver Operating Characteristic Curves
Qualitative
  • Diagnostic performance for 1 test, 2 paired tests, or 2 independent tests/groups
  • Sensitivity/Specificity with Clopper-Pearson exact or Wilson score confidence interval
  • Likelihood ratios with Miettinen-Nurminen score confidence interval
  • Predictive values with Mercado-Wald logit confidence interval
  • Odds ratio, and Youden's index
  • Mosaic plot of outcomes
  • Difference between sensitivity/specificity with Newcombe or Tango score confidence intervals.
  • McNemar-Mosteller exact, Fisher exact, and Score Z test for equality of sensitivity/specificity
  • Supports CLSI EP12-A2 User Protocol for Evaluation of Qualitative Test Performance

Included from the Analyse-it Standard edition...

Distribution
Continuous
  • Sum, Mean, Variance, SD, CV%, Skewness, Kurtosis
  • Geometric Mean, Harmonic Mean new in v4.50
  • Median, Minimum, Maximum, Range, IQR
  • Quantiles
  • Mode
  • Histogram with optional normal overlay
  • Frequency polygon
  • Dot plot – jittered, aligned, spread points and vary point symbol/color
  • Skeletal box plot, Tukey outlier box plot, Quantile box plot
  • Mean error bar plot, Mean confidence diamond plot
  • CDF plot with optional Kolmogorov-Smirnov confidence band
  • Normal Q-Q plot with optional Lilliefors confidence band
  • Shapiro-Wilk, Anderson-Darling, and Kolmogorov-Smirnov tests for normality
  • Z test for mean with known population SD
  • Student’s one sample t-test for mean
  • Wilcoxon test for mean/median
  • Sign test for median
  • X2 test for variance
  • Mean estimate with t-based or Z-based confidence interval
  • Median with Thompson-Savur confidence interval
  • Hodges-Lehmann pseudo-median with Tukey confidence interval
  • Variance with X2-based confidence interval
Discrete
  • Frequency table – frequency, cumulative frequency, relative frequency, cumulative relative frequency
  • Frequency bar plot with optional cumulative frequency line
  • Frequency pie plot
  • Binomial exact test for proportions
  • Score Z test for binomial proportions
  • Pearson X2 and Likelihood ratio G2 test for multinomial proportions
  • Proportion with Clopper-Pearson exact or Wilson score confidence interval
Compare groups
  • Descriptive statistics by group
  • Side-by-side dot plots, mean plots, box plots by group
  • Z test for difference in means with known population SDs
  • Student’s independent samples t-test for difference between means
  • Welch’s t-test for difference between means with unequal variances
  • Wilcoxon-Mann-Whitney test for difference between means/medians
  • 1-way between-subjects ANOVA for equality of means
  • Welch’s ANOVA for equality of means with unequal variances
  • Kruskal-Wallis test for equality of mean/medians
  • Mean difference with t-based, Welch-Satterthwaite t-based, or Z-based confidence interval
  • Cohen’s and Hedge’s g standardized mean difference with non-central t-based confidence interval
  • Hodges-Lehmann location shift with Moses confidence interval
  • Multiple comparisons procedures: Student’s t (individual comparisons), Tukey-Kramer (all pairs), Dunnett (against control), Hsu (with best), Scheffe (all contrasts), Steel (non-parametric against control), Dwass-Steel-Critchlow-Fligner (non-parametric all pairs), Wilcoxon (non-parametric individual comparisons)
  • Mean-Mean scatter plot for multiple comparisons
  • F-test for variance ratio
  • Bartlett, Levene, and Brown-Forsythe tests for homogeneity of variances
Compare pairs
  • Descriptive statistics for each group
  • Side-by-side dot plots, mean plots, box plots by group
  • Difference plot with identity line and optional histogram of differences
  • Z-test for difference in means with known population SDs
  • Student’s paired samples t-test for difference between means
  • Wilcoxon signed ranks test for difference between means/medians
  • Sign test for difference between medians
  • 1-way within-subject ANOVA for equality of means
  • Friedman test for equality of medians
  • Mean difference with t-based or Z-based confidence interval
  • Cohen’s and Hedge’s g standardized mean difference with non-central t-based confidence interval
  • Median difference with Thompson-Savur confidence interval
  • Hodges-Lehmann location shift with Tukey confidence interval
Contingency tables
  • Contingency table
  • Grouped frequency plot
  • Stacked frequency plot
  • Pearson X2 test for independence / equality of proportions
  • Likelihood ratio G2 test for independence
  • Mosaic plot for association with color by category or residual
2x2 related tables
  • McNemar-Mosteller exact test for symmetry / marginal homogeneity
  • Score Z test for difference between proportions
  • Proportion difference (risk difference) with Newcombe score or Tango score confidence interval
  • Odds ratio with Binomial exact or Wilson score confidence interval
2x2 tables
  • Fisher exact test for independence
  • Score Z test for difference between proportions
  • Proportion difference (risk difference) with Miettinen-Nurminen score, Newcombe score, or Tango score confidence interval
  • Proportion ratio (risk ratio) with Miettinen-Nurminen score or Newcombe confidence interval
  • Odds ratio with Hypergeometric exact or Miettinen-Nurminen score confidence interval
Fit model – ANOVA / ANCOVA / Regression
Linear Fits
  • Simple linear regression
  • Polynomial regression (2nd to 6th order)
  • Logarithmic regression
  • Exponential regression
  • Power regression
  • Multiple linear regression
  • ANOVA new in v4.80
  • ANCOVA new in v4.80
  • Advanced models with simple, crossed, polynomial and factorial terms, with categorical explanatory variables coded as dummy variables
Other Fits
  • Binary logistic regression
  • Model equation
  • Summary of fit – R2, AIC, BIC
  • Parameter estimates – beta, confidence intervals, VIF, standardized beta
  • Scatter plot with fit line and optional confidence bands
  • F-test effect of model
  • Predicted against actual plot
  • F-test effect of each term in model
  • Leverage plot for effect of each term
  • Residual plot – raw, standardized
  • Sequence and Lag-1 plots
  • Outlier and Influence plot
  • Cook's D influence
  • Predict Y for X
  • Effect means for categorical variables new in v4.80
  • Main effect and interaction plots for categorical variables new in v4.80
  • Multiple comparisons of effect means: Student’s t (individual comparisons), Tukey-Kramer (all pairs), Dunnett (against control), Hsu (with best), Scheffe (all contrasts) new in v4.80
  • F-test for lack of fit test for simple regression models
  • Save model variables back to the dataset: Fitted Y, Residuals, Standardized Residuals, Studentized Residuals, Leverage, Cook's Influence
Multivariate
  • Correlation matrix with color map on coefficients
  • Covariance matrix
  • Scatter plot
  • Scatter plot matrix
  • Vary points by color of based on a factor
Correlation / Association
  • Pearson r correlation coefficient with Fisher’s Z confidence interval
  • Pearson test for linear association
  • Spearman rs correlation coefficient with Fisher’s Z confidence interval
  • Kendall tau correlation coefficient with Samara-Randles confidence interval
  • Kendall test for monotonic association
Item reliability new in v4.80
  • Cronbach’s alpha (standardized and unstandardized)
  • Deleted Cronbach’s alpha for each item
PCA new in v3.80
  • Eigenvalues / Eigenvectors
  • Coefficient matrix with color map to reveal relationships
  • Classic Gabriel bi-plot with variables as vectors and observations as points
  • Gower-Hand bi-plot with variables and observations as points
  • Predict new observations / variables
  • Reflect, rotate, and scale bi-plot
  • Correlation mono-plot to show relationship between variables
  • Scree plot
Common Factor Analysis new in v3.90
  • Maximum likelihood factor extraction
  • Factor pattern / structure matrices with color map to reveal structure
  • 12 factor orthogonal/oblique rotations including Varimax, Oblimin