The meaning of diagnostic test results: a spreadsheet for swift data analysis.
OBJECTIVE
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.


METHODS
Microsoft Excel(TM)was used. Section A: a contingency (2 x 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(TM)version can be downloaded via the Internet from the URL ftp://radiography.com/pub/Rad-data99.xls


CONCLUSIONS
A spreadsheet is useful for contingency table data analysis and assessment of the clinical meaning of diagnostic test results.
Maceneaney
P M
PM
Department of Radiology, St. Vincent's University Hospital, Elm Park, Dublin 4, Ireland.
Malone
D E
DE
D001499:Bayes Theorem; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D011237:Predictive Value of Tests; D011871:Radiology; D012680:Sensitivity and Specificity; D012984:Software