Methods for analysing cardiovascular studies with repeated measures

Background. Repeated measurements in a single subject are generally more similar than unrepeated measurements in different subjects. Unrepeated analyses of repeated data cause underestimation of the treatment effects. Objective. To review methods adequate for the analysis of cardiovascular studies with repeated measures. Results. (1) For between-subjects comparisons, summary measures and random-effects mixed-linear models are possible. Examples of summary measures include the area under the curve of drug time-concentration and time-efficacy curves, maximal values, mean values, and changes from baseline. A problem is that precision is lost because averages, rather than individual data, are applied. Random-effects mixed-linear models, available in SPSS statistical software and other software programmes, provide better precision for that purpose. (2) For within-subjects comparisons, repeated-measures ANOVAs are available in SPSS and other software programmes. Subgroup factors such as gender differences and age class can be included. Discussion. For non-Gaussian data, Wilcoxon's and Friedman's tests are available, for binary data McNemar's tests can be used in case of two repeated observations. No standard methods are available for repeated binary measures with more than two observations. The purpose of this review was not to present a complete report but, rather, to underline that ample efforts should be made to account for the special nature of repeated measures. (Neth Heart J 2009;17:429-33.)