title: Confidence in Altman-Bland plots: a critical review of the method of differences.
abstract: 1. Altman and Bland argue that the virtue of plotting differences against averages in method-comparison studies is that 95% confidence limits for the differences can be constructed. These allow authors and readers to judge whether one method of measurement could be substituted for another. 2. The technique is often misused. So I have set out, by statistical argument and worked examples, to advise pharmacologists and physiologists how best to construct these limits. 3. First, construct a scattergram of differences on averages, then calculate the line of best fit for the linear regression of differences on averages. If the slope of the regression is shown to differ from zero, there is proportional bias. 4. If there is no proportional bias and if the scatter of differences is uniform (homoscedasticity), construct 'classical' 95% confidence limits. 5. If there is proportional bias yet homoscedasticity, construct hyperbolic 95% confidence limits (prediction interval) around the line of best fit. 6. If there is proportional bias and the scatter of values for differences increases progressively as the average values increase (heteroscedasticity), log-transform the raw values from the two methods and replot differences against averages. If this eliminates proportional bias and heteroscedasticity, construct 'classical' 95% confidence limits. Otherwise, construct horizontal V-shaped 95% confidence limits around the line of best fit of differences on averages or around the weighted least products line of best fit to the original data. 7. In designing a method-comparison study, consult a qualified biostatistician, obey the rules of randomization and make replicate observations.
authors.affiliation: Department of Surgery, The University of Melbourne, Parkville, Victoria, Australia. ludbrook@bigpond.net.au
authors.forename: John
authors.initials: J
authors.lastname: Ludbrook
mesh_terms: D015982:Bias; D001794:Blood Pressure; D045853:Exhalation; D005260:Female; D006801:Humans; D016014:Linear Models; D008895:Milk, Human / Q000737:chemistry; D015233:Models, Statistical; D012107:Research Design / Q000706:statistics & numerical data; D013223:Statistics as Topic