Clinical Trials

❘❙❚■ Chapter 25 | Adjustment for CovariatesTable 4. Advantages and disadvantages of an adjusted analysis.AdvantagesImbalances are accounted for in known prognosticfactor(s) across treatment groups at baseline. Failureto control for such factors can lead to a biasedestimate of the true treatment effect [2,3].Increased precision of the estimated treatment effectwith normal outcomes modeled using regressionmodels: adjustment for baseline imbalances willresult in increased efficiency as explained variation issubtracted [4,5].Reduction in bias with non-normal outcomes modeledusing logistic or Cox regression models: in logisticregression, for example, the adjusted analysis yieldsa larger standard error of the odds ratio estimate fora treatment effect than the unadjusted analysis, butthis could be more than offset by a more accurateestimate of the odds ratio [6,7].DisadvantagesChoosing the covariates to be adjusted is inherentlysubjective since many plausible analyses are possible.Therefore, different results can be generated usingdifferent covariates.Covariates that are not collected at baseline but havea substantial impact on the primary endpoint cannotbe accounted for in the adjusted analyses.The simplicity of interpreting the treatment differenceobtained from unadjusted analyses is lost and resultsare harder to describe – eg, the estimated treatmenteffect from an unadjusted analysis of a two-way paralleltrial can be interpreted as the difference in the primaryendpoint between two patient populations receiving twodifferent treatments. On the other hand, it is difficultto generalize the results obtained from an adjustedanalysis since, eg, the estimated treatment effect takesinto account peculiar characteristics of the data at hand.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 (such as ANCOVA) can be used to adjust forany imbalances, in particular baseline measurements of the outcome variable.Simulation studies have shown that this method has a higher statistical powerfor detecting a treatment effect compared to other approaches, such as the useof change (or percentage change) from baseline as a derived outcome in theanalysis [8,9].For binary outcome data, either a stratified analysis or a logistic regression modelcan be employed. In a stratified analysis, the treatment effect is estimatedseparately across the subgroups of a prognostic factor. The Mantel–Haenzelmethod permits the combining of subgroups, giving more weight to strata withmore information and providing an adjusted overall estimate of the treatmenteffect. The advantage of such an analysis is the clarity of presentation, while themajor limitation is that only a small number of covariates can be considered.Finally, if the outcome is survival time, a Cox regression model should be used,as illustrated in the PBC trial. The adjusted hazard ratio is often compared withthe unadjusted hazard ratio to assess the impact of any imbalances of baselinevariables on the estimates of the treatment effect.292