Clinical Trials

❘❙❚■ Chapter 28 | Repeated MeasurementsFor example, in a study comparing coronary-artery bypass surgery with andwithout cardio-pulmonary bypass and cardiac arrest (on- and off-pump surgery),Khan et al. compared mean troponin T levels for off-pump and on-pump patientsat 0, 6, 12, 24, 48, and 72 hours after randomization [7]. It was found that at6 and 12 hours postoperatively, troponin T levels were significantly higher in theon-pump group than the off-pump group (P < 0.001 for both comparisons),but this difference disappeared by 24 hours.It should be noted that although analysis at each time point is often requested byclinicians, this method of analysis should not be encouraged because false-positiveresults may be generated due to multiple testing (ie, a significant result may befound due to chance). In addition, this method ignores within-subject correlation.If a significant difference is found between treatment arms at one time point,differences are likely to be significant at subsequent time points. Hence, time-bytimeanalysis is best used when there are a small number of time points and theintervals between them are large [5].Use of statistical modelsThe use of statistical models in the analysis of repeated measurement data isbecoming increasingly popular [1–3,5,6]. These models can be particularly usefulwhen the object of the study is to assess the average treatment effect over theduration of the trial. During the last 20 years, statisticians have considerablyenriched the methodology available for the analysis of such data [1,5].These models offer a variety of approaches for handling both correlation betweenrepeated measurements and missing values. Of these, the mixed model has beenwidely used in the analysis of repeated measurement data in clinical trials, for tworeasons [2,5]:• it can accommodate a wide variety of ways in which the successiveobservations are correlated with one another• it does not require complete data from all subjectsUse of summary measuresThe most straightforward method of analyzing repeated measurement data isto use a summary measure. This method is sometimes known as response profileanalysis [5,6]. There are two steps to a summary measure approach. The first stepis to calculate a summary statistic from the repeated measurements for eachsubject, eg, the mean, maximum, area under the curve (AUC). The second step isto compare the difference in the summary statistic by treatment groups, by usinga standard statistical technique such as a t-test, a nonparametric test, or ananalysis of variance.320