Abstract AIMS: To design a spreadsheet program to: (a) analyse rapidly diagnostic test result data produced in local research or reported in the literature; (b) correct reported predictive values for disease prevalence in any population; (c) estimate the post-test probability of disease in individual patients. MATERIALS AND METHODS: Microsoft Excel TMwas used. Section A: a contingency (2 × 2) table was incorporated into the spreadsheet. Formulae for standard calculations [sample size, disease prevalence, sensitivity and specificity with 95% confidence intervals, predictive values and likelihood ratios (LRs)] were linked to this table. The results change automatically when the data in the true or false negative and positive cells are changed. Section B: this estimates predictive values in any population, compensating for altered disease prevalence. Sections C–F: Bayes' theorem was incorporated to generate individual post-test probabilities. The spreadsheet generates 95% confidence intervals, LRs and a table and graph of conditional probabilities once the sensitivity and specificity of the test are entered. The latter shows the expected post-test probability of disease for any pre-test probability when a test of known sensitivity and specificity is positive or negative. RESULTS: This spreadsheet can be used on desktop and palmtop computers. The MS Excel TMversion can be downloaded via the Internet from the URL ftp://radiography.com/pub/Rad-data99.xls CONCLUSION: A spreadsheet is useful for contingency table data analysis and assessment of the clinical meaning of diagnostic test results.MacEneaney, P. M., Malone, D. E. (2000). Clinical Radiology 55, 227–235.