How to handle large baseline difference in a randomized trial I have results of a randomized trial where 200 participants were randomly allocated to 2 intervention groups and outcome evaluated 1 month later.
I believe baseline differences between 2 groups are now not subjected to hypothesis tests. However, I find that one of the baseline variables is clearly different in 2 groups and this difference gives P value <0.001 on hypothesis testing. Moreover, this baseline parameter may influence the outcome parameter, hence all observed difference in outcome may not be just due to different interventions tested.
How do I handle this? Should I just ignore baseline difference? Should I use this baseline parameter as a covariate in outcome analysis? Thanks for your insight.
 A: As @MichaelLew notes, there could have been a failure of randomization.  If the interventions have not been applied, you could try rerandomizing.  Assuming it wasn't, bear in mind that this does (and should) happen all the time, especially if a large number of baseline variables are checked (it can be common to check dozens, and we would expect 1 in 20 to be 'significant').  There are two different things to bear in mind:

*

*Since the groups were formed by randomizing, it is nonsensical to test for differences.  Hypothesis tests try to ascertain if the groups came from different populations (viz., populations with different means).  But we know a-priori that they came from the same population (those who consented into the study) because we assigned them to the groups from the same initial pool.
That doesn't mean it's bad to check for covariate balance, only to test for it.  As I said, covariates are uncorrelated with your intervention in the population, but not in your sample.  So don't look at p-values, look at measures of effect size.  In a small study, you could (by chance alone) have a large and important covariate imbalance, without it being significant.  Likewise, in a large study the random deviations from pure balance will result in some type I errors that are trivial and ignorable.
After computing effect sizes, I try to consult with the PI's on the study to see if any of the imbalances would be considered meaningful.  These could be variables with a known causal relationship with the response, for example.  In which case, they may care about small differences.


*Let's imagine then, that you have covariate imbalance that is large and/or meaningful.  (This seems to be the crux of your question.)  In that case, you just control for those variables in the final model.  Simply including them as covariates is sufficient.
