Clinical Trials

Clinical Trials: A Practical Guide ■❚❙❘In addition, there might be differences between groups in one or more baselinevariables that are not statistically significant, but that are clinically important –particularly if they affect the outcome. Therefore, statistical testing leading to anonsignificant difference might give false assurance about the differences betweengroups. The contents of the table of baseline measurements and any relevantdifferences should be discussed in the results section of a manuscript.What can be done if imbalances occur?It is important to perform significance testing if it is suspected that therandomization or blinding procedure is flawed in some way [6]. For baselinecharacteristics that are predictors of outcome variables, if important imbalancesare found then covariate adjustment analysis should be performed to estimateadjusted treatment effects [5,7]. This is often done with multivariate regressionmethods during analysis, which take into account confounding factors andimbalances in relevant variables at baseline.There are a number of regression methods available and the choice of methoddepends on the type of outcome variable. For example, if the outcome variable iscontinuous, a linear regression model (including analysis of covariance) can beused during the analysis to adjust for any imbalances and potential confoundersthat occur despite randomization [5]. If the outcome is binary then a logisticregression model should be employed. When the outcome is survival time, a Coxregression model is usually used.ConclusionBaseline data are crucial for describing the study population and establishing theexternal validity of a trial. It is particularly important to include demographicvariables and any measurements taken at randomization that might have animpact on the treatment effect. By comparing the distributions of several baselinevariables according to treatment group, we can provide a clear picture of thepatients included and identify imbalances that have arisen by chance. Theoccurrence of severe imbalances in a trial might suggest failure of therandomization or blinding procedures. In this case, covariate adjustment analysesmust be made to calculate the unbiased treatment effect. This topic is discussedfurther in Chapter 25.389