Peter <[email protected]>:
No * SOAP BOX * required--I agree that if you did the randomization
job right, there is no need to test for balance, except perhaps to
convince your readers you did not get extremely unlucky in the
randomization process, or to reassure them you did the randomization
right. And you should probably be adjusting for covariates in some
way anyway, not just to reduce bias in estimating average treatment
effects, but to improve efficiency! Most of these points were made in
a very cogent way in the 1920's by Fisher:
http://digital.library.adelaide.edu.au/coll/special//fisher/48.pdf
(note the nice statement of the hypothetical counterfactual or
potential outcome on the second page and see also
http://www.jstor.org/stable/2245382 and Rubin's remarks).
On Wed, Oct 1, 2008 at 11:29 AM, Lachenbruch, Peter
<[email protected]> wrote:
> The tests Dr. Nichols notes assume normality - not much of an issue for univariate issue unless there is bad skewness. The multivariate test based on Hotelling could be an issue as it isn't quite as robust to non-normality.
>
> The testing of balance after randomization is often done in the pharmaceutical industry but Senn had a good article in Statistics in Medicine on this about 10 years ago. It's not sensible, as all it does is verify if you did the job right, and if you didn't what then?
>
> Others have suggested using the test to determine if you should adjust for the variables that aren't balanced. This is allowing the data to determine the analysis, and is completely exploratory. If you are planning to adjust for covariables, you should specify these a priori - and usually these are based on their potential effect on the response.
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