Adjusting for baseline factors

We saw in the previous chapter how to account for centre in treatment comparisons using two-way ANOVA for continuous data and the CMH test for binary, categorical and ordinal data. These are examples of so-called adjusted analyses, we have adjusted for centre differences in the analysis. 

There may be other factors that we wish to adjust, for example, age, sex, baseline risk and so on and there are several reasons why we might want to do this. 

Firstly, if the randomisation has been stratified for baseline variables then from a theoretical statistical point of view these variables should be taken into account in the analysis. Secondly, the efficiency of the statistical analysis can be improved in several ways if baseline prognostic factors (factors which influence outcome) are included in the analysis. Finally, it provides a framework for the investigation of the consistency of the treatment effect according to different values for those factors. 

As an example, suppose that the randomisation has been stratified on the basis of age and sex with four strata  

An adjusted (or stratified) analysis for a continuous outcome variable would be two-way ANOVA. The four strata would be handled in exactly the same way as if there were four centres in a multi-centre trial. The ANOVA approach will also 

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