Clinical Trials

❘❙❚■ Chapter 34 | Presenting Baseline Datatreatment. The baseline data included demographic variables (eg, age, gender)and factors that were likely to affect outcome (eg, left ventricular ejection fraction,number of diseased vessels, presence of comorbidities such as history of stroke,smoking, and diabetes mellitus). Factors that could modify any benefit of thetreatment (eg, current medication) should also be reported.If a large study includes subgroup analyses then the characteristics of thesesubgroups should be included. For example, in a surgical study of aortic valvereplacement, patients receiving CABG might have a poorer outcome than thosenot receiving concomitant CABG, since a longer, more complex operation isrequired. From the baseline data provided, the reader can determine whether thecharacteristics of the subgroup receiving CABG with valve replacement aresimilar to those of the patients receiving valve replacement only.The measurement of the baseline characteristics should also be clear. For example,in a trial on hypercholesterolemia, when reporting baseline lipid measurements itshould be specified if the patients are fasting. Similarly, in a trial on hypertensionit should be stated whether average blood pressure was assessed by a 24-hourambulatory blood pressure monitor, or as measured in a clinic.Continuous variables, such as age or height, should be reported as a mean valueand the standard deviation should be given. If the data are not normallydistributed (ie, skewed) then the median and range should be reported.Categorical variables, such as gender or ethnic group, should be reported asfrequencies and percentages.It is very important not to include too many variables in the table of baseline dataas this makes interpretation of the data confusing for the reader. It isrecommended that giving all patient data as well as individual group data shouldbe avoided and also that significance tests are not included in the table.Should significance tests be carried out?The use of significance tests when comparing treatment groups for imbalances inbaseline characteristics is a controversial issue [3]. Many statisticians argue that ifrandomization has been performed correctly then it is not necessary to carry out astatistical analysis of differences in baseline measurements between groups [4]. Thisis because the more variables are tested, the more likely it is that one variable will befound to be significantly imbalanced between treatment groups by chance alone(see Chapter 29). In this sense, the comparison of baseline data is a misuse ofsignificance testing since it is not being used to test a useful scientific hypothesis [4,5].388