Medical Edition

The tools behind better diagnostic decisions Diagnostic accuracy with ROC curves and AUC comparison, Bland–Altman method agreement, reference intervals, survival analysis with Kaplan–Meier and Cox regression — plus the full general statistics toolkit for hypothesis testing, ANOVA, and regression.

Clinical researchers, laboratory scientists, and biostatisticians need specialised analyses that general-purpose statistics packages either don’t include or bury in add-on modules. ROC curves for evaluating a diagnostic test, Bland–Altman for comparing measurement methods, reference intervals for establishing normal ranges, survival analysis for time-to-event data — alongside the t-tests, ANOVA, regression, and descriptive statistics that support every study.

The Medical edition brings all of this together in one package inside Excel. No separate applications for different analyses, no data export, no cloud processing. Cited in 18,000+ peer-reviewed publications — most of them clinical research.

Analyse-it has helped tremendously. Previously I used Prism and Microsoft Excel, but Analyse-it has made my life so much easier and saved so much time.
Man Khun Chan, M.Sc., ART
Test Development Medical Technologist
The Hospital For Sick Children, Toronto, Canada

Evaluate diagnostic test accuracy

Establish and compare the ability of a diagnostic test to discriminate between patients with and without a condition — and find the decision threshold that balances sensitivity, specificity, and clinical cost:

  • Empirical and binormal ROC curves with DeLong AUC and confidence intervals
  • AUC comparison (DeLong) for up to 10 paired or independent tests
  • Sensitivity, specificity, likelihood ratios, predictive values, diagnostic odds ratio
  • Optimal threshold — Youden, closest-to-(0,1), cost-based
  • Decision plots across all possible thresholds
  • Qualitative test evaluation — PPA/NPA, kappa, weighted kappa
Diagnostic accuracy details →
ROC curve with AUC comparison and decision plot

Compare method agreement

When introducing a new analyser or comparing a point-of-care device against a laboratory method, see whether the two methods agree well enough for clinical use:

  • Bland–Altman limits of agreement with mean, median, and linear fit bias
  • Constant and non-constant precision (regression-based V-shaped limits)
  • Mountain plot with allowable difference band
  • Singlicate, duplicate, and replicate measurements with within-subject variance estimation
  • Qualitative agreement — PPA/NPA, kappa, weighted kappa
Bland–Altman agreement details →
Bland-Altman difference plot with limits of agreement

Establish reference intervals for clinical interpretation

Full range of reference interval methods — match the method to your sample size and distribution, partition where subgroups need separate ranges, and transfer or verify intervals when moving to a new measurement procedure:

  • Five quantile methods — parametric, non-parametric (three computation approaches), robust bi-weight, bootstrap, and Harrell–Davis
  • Partition by sex, age, ethnicity, or any combination of factors
  • Full range of transformations — log, square root, cube root, Box–Cox, Manly exponential, two-stage exponential/modulus
  • Outlier screening with Tukey box plots, Shapiro–Wilk and Anderson–Darling normality tests
  • Transfer and verify existing intervals using regression or binomial proportion test
Reference interval details →
Reference intervals with histogram and reference limits

Analyse time-to-event data with survival analysis

Estimate survival functions, compare treatment groups, and model the effect of covariates on the hazard:

  • Kaplan–Meier survival curve with pointwise, Nair, or Hall–Wellner confidence bands
  • Median, quartile, and restricted mean survival time
  • Test equality of survival functions with log-rank, Wilcoxon, Tarone–Ware, Fleming–Harrington
  • Cox proportional hazards regression — hazard ratios with confidence intervals, baseline survival function
  • Log and log–log diagnostic plots for assessing the proportional hazards assumption
Survival analysis details →
Kaplan-Meier survival curves

Compare treatment groups and test for differences

The baseline demographics table, the primary endpoint comparison, the subgroup analysis — hypothesis testing is the backbone of clinical research:

  • Student’s t, Welch’s t, Wilcoxon-Mann-Whitney for two groups; one-way ANOVA, Welch’s ANOVA, Kruskal-Wallis for three or more
  • Paired t-test, Wilcoxon signed ranks, Sign test, within-subjects ANOVA, Friedman
  • Nine multiple comparison procedures — Tukey-Kramer, Dunnett, Hsu, Scheffé, Steel, Dwass-Steel-Critchlow-Fligner
  • Cohen’s d and Hedges’ g effect sizes with non-central t confidence intervals
  • Contingency tables — χ², Fisher exact, McNemar, odds ratios, risk ratios
Hypothesis testing details →
Compare groups with ANOVA and multiple comparisons

Model risk factors and outcomes with regression

Simple and multiple linear regression, logistic regression with odds ratios, ANOVA and ANCOVA — with the diagnostics to know whether you can trust the result:

  • Simple, multiple, polynomial, logarithmic, exponential, power, and probit regression
  • Binary logistic regression with odds ratios and Wald confidence intervals
  • ANOVA and ANCOVA with effect means, interaction plots, and multiple comparisons
  • Residual diagnostics, leverage plots, Cook’s D influence, VIF for multicollinearity
  • Predicted values, main effect plots, interaction plots
Regression details →
Multiple regression with leverage plots

Describe and summarise your data

Every analysis starts with understanding the distribution:

  • Mean, median, SD, CV%, skewness, kurtosis, geometric mean, quantiles
  • Histograms, box plots, dot plots, Q-Q plots with Lilliefors bands, CDF plots
  • Shapiro-Wilk, Anderson-Darling, and Kolmogorov-Smirnov normality tests
  • One-sample t-test, Wilcoxon, Sign test, χ² test for variance
  • Correlation — Pearson r, Spearman rs, Kendall τ with confidence intervals
  • PCA and common factor analysis with biplots and 12 rotation methods
Descriptive statistics details →
Distribution analysis with histogram and box plot
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.
Marco Balerna, Ph.D.
Clinical Chemist
Ente Ospedaliero Cantonale, Switzerland
Read the case study →

Includes the full Standard edition

The Medical edition includes every feature from the Standard edition — the general-purpose statistics toolkit that clinical researchers use for the rest of the study. Baseline demographics, treatment group comparisons, outcome modelling, and exploratory analysis are all in the same workbook as your Bland–Altman, ROC, and survival analyses.



Software you can trust

Validated calculations you can defend at peer review Every calculation is performed by Analyse-it — no Excel formulas, no third-party functions. Results are validated against published datasets and thousands of internal test cases before every release. See how we develop and validate Analyse-it →
Patient data stays on your PC Analyse-it runs entirely within Microsoft Excel on your PC. No cloud processing, no data transmission. Patient data and research data 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 co-authors, attach to a manuscript submission, archive for publication queries. No proprietary format, no licence required to view results. Co-authors and reviewers see exactly what you see.
Results that can’t 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 published are exactly what you’ll find when you reopen the workbook months or years later when a reviewer asks.

Example analyses

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

EP24 A2 Example 1 Diagnostic accuracy
OxLDL and LDL, two paired tests. ROC curves with DeLong AUC and CIs. AUC comparison. Bi-histogram and decision plot.
EP21 A Example 1 Method agreement
LDL Cholesterol, 100 observations. Mountain plot, limits of agreement, allowable difference ±10 mg/dL.
EP28 A3 Example 1 Reference intervals
Calcium, partitioned by sex. Non-parametric (N+1)p, 120 per group. Histogram with reference limits and 90% CIs.
Kaplan Meier Survival analysis
Kaplan–Meier, two treatment groups. Survival curves with confidence bands. Log-rank test. Median survival with CIs.
EP12 A2 Example 1 Qualitative test evaluation
H. pylori, two paired tests. Sensitivity, specificity, predictive values with Wilson CIs. Score Z test.
Compare Groups 2 Compare groups
Y by brand, 7 groups. One-way ANOVA, Tukey-Kramer all-pairs, Mean-Mean scatter plot.
Fit Model 2 Multiple regression
Pulse rates, 109 observations, 8 predictors. VIF, leverage plots, residual diagnostics, outlier/influence plot.
Fit Model 3 Logistic regression
ICU patient survival, 17 predictors. Odds ratios, Wald CIs, G² likelihood ratio tests.

Technical details

Diagnostic performance

ROC analysis

  • 1 test, up to 10 paired tests, or up to 10 independent tests/groups
  • Empirical and binormal ROC curves
  • ROC curve plot with confidence band
  • Overlaid ROC curves for test comparison
  • Wilcoxon–Mann–Whitney AUC with DeLong–DeLong–Clarke–Pearson CI
  • Z test of AUC is better than chance
  • Partial AUC over specified FPR range
  • Compare DeLong AUC difference — equality, equivalence, or non-inferiority
  • 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
  • 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 new in v4.0
  • Predict FPF at fixed sensitivity, sensitivity at fixed FPF, or sensitivity/FPF at fixed threshold

Qualitative test evaluation

  • 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 with Mercado–Wald logit CI
  • Diagnostic odds ratio and Youden index
  • Proportion in positive/negative agreement (PPA/NPA)
  • Kappa and weighted kappa with Wald Z CI
  • Mosaic plot of outcomes
  • Difference between sensitivity/specificity with Newcombe or Tango score CI
  • McNemar–Mosteller exact, Fisher exact, and score Z test

Agreement (Bland–Altman)

Quantitative methods

  • Singlicate, duplicate, and replicate measurements
  • Bland–Altman limits of agreement with mean, median, and linear fit bias new in v3.75
  • Limits of agreement — constant and non-constant precision
  • Confidence intervals on limits of agreement
  • Allowable difference specification and overlay
  • Within-subject variance estimation from replicates
  • Precision (SD or CV) for each method
  • Pearson r correlation coefficient

Qualitative methods

  • Proportion in positive/negative agreement with Clopper–Pearson exact or Wilson score CI
  • Kappa and weighted kappa with Wald Z CI
  • Kappa test for agreement

Plots

  • Scatter plot with identity line
  • Difference / relative difference / ratio plot with allowable difference band and histogram
  • Mountain plot with allowable difference band new in v3.71
  • Vary colour of points by a factor

Reference intervals

Establish reference limits

  • Normal (parametric) quantile
  • 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

Transfer / verify

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

Partitioning & transformations

  • Partition by factor(s) new in v4.00
  • Reciprocal, log, square and cube root
  • Box–Cox new in v3.52
  • Manly exponential new in v4.00
  • Two-stage exponential / modulus new in v4.00

Normality & outliers

  • Shapiro–Wilk test
  • Anderson–Darling test
  • Normal Q–Q plot with Lilliefors confidence band
  • Frequency histogram with normal overlay and reference limits
  • Tukey outlier box plot

Survival / reliability

Survival function new in v6.10

  • Kaplan–Meier survival curve with pointwise, Nair, or Hall–Wellner confidence bands
  • Median and quartiles for survival/failure
  • Mean (area under survival curve) and restricted mean
  • Survival/failure probabilities and confidence intervals
  • Test equality: log-rank, Wilcoxon, Tarone–Ware, Fleming–Harrington
  • Transformed log and log–log S(t) vs time plots

Proportional hazards new in v6.10

  • Model equation
  • Parameter estimates and Wald confidence intervals
  • Covariance of estimates
  • Baseline survival function and plot
  • Likelihood ratio and Wald test of model and terms
  • Hazard ratio at each level / specified unit change and at specific levels of interacting covariates

Included from the Standard edition

The Medical edition includes every feature from the Standard edition. See the Standard edition page for the full technical specification, including:

  • Descriptive statistics, histograms, box plots, dot plots, Q–Q plots, normality tests
  • Compare groups: t-test, Welch, Wilcoxon, ANOVA, Kruskal–Wallis, 9 multiple comparison procedures
  • Compare pairs: paired t-test, Wilcoxon signed ranks, Friedman, within-subject ANOVA
  • Effect sizes: Cohen’s d, Hedges’ g, Hodges–Lehmann
  • Contingency tables: Pearson χ², Fisher exact, McNemar, odds/risk ratios
  • Simple, multiple, polynomial, logistic regression with diagnostics
  • ANOVA/ANCOVA with effect means, interaction plots, multiple comparisons new in v4.80
  • Correlation: Pearson r, Spearman rs, Kendall τ
  • PCA, common factor analysis, biplots, 12 rotation methods
  • Cronbach’s alpha new in v4.80

System requirements

  • Microsoft Excel 2013, 2016, 2019, 2021, 2024 and Microsoft 365 for Microsoft Windows (32- and 64-bit)
  • Microsoft Windows 8, 10, 11, Server 2016, 2019, 2022
  • 2 GB RAM minimum recommended
  • 80 MB disk space