Some statisticians do not like to perform adjusted analyses such as ANCOVA in comparative efficacy (Phase III) trials because they feel that randomization and proper analysis guarantee unbiasedness and the correctness of type I error levels, even if there are chance imbalances among the treatment groups with respect to prognostic factors.
This may be true, but the use of prognostic factors in ANCOVA models can improve precision and verify biological information.
Investigators should consider using a prognostic factor as a covariate in the analysis of data from a randomized trial under any of the following circumstances:
- the prognostic factor is significantly unbalanced among the treatment groups;
- the prognostic factor is strongly associated with the outcome variable, in the presence of balance (i.e., stratifiers) or imbalance among treatment groups;
- it is of interest to determine whether the prognostic factor causes or reduces the treatment effect;
- the prognostic factor is clinically important and it is of interest to illustrate and quantify its effect.