stratification and minimization are two randomization options to achieve balance in terms of covariates or baselines in small RCTs. in recent years, researchers seem to have favoured minimization, particularly for sequential allocation designs with a high number of balancing variables. in short, the method involves the choice of some imbalance criterion and then sequentially allocates each new individual to the study arm leading to the smallest new criterion value. this allocation can be done purely deterministically or involving some element of chance.

our question is if the approach could be simplified when the assignment to study arms takes place only after inclusion of all participants is completed. our idea is to use simple randomization without any constraints to generate a large number, say N = 1000, of complete and fully random allocation schemes. in the next step we would identify the, say, n = 100 schemes with the smallest imbalances (using a similar criterion as for minimization) or, alternatively, all schemes with a criterion value below some prespecified cut-off value. finally, we would choose one of these remaining schemes at random.

the whole process would be carried out by a third person not involved in the study intervention or the collection of study outcomes. the investigators would only receive the last resulting allocation scheme from the person responsible for allocation.

the rationale behind our idea is simplicity and that we would like not to sacrifice too much randomness for balance.

has anyone heard of such an allocation strategy before? what do you think of it? are there any considerations concerning bias? (in some way, we would just be rejecting allocation schemes as long as we don't like them because of intolerably low balance. on the other hand, minimization or even stratification contain similar aspects...) and what about the implications for the statistical analysis? would you still adjust for the covariates using covariance analysis? any other thoughts?

thank you very much for any feedback!

  • $\begingroup$ This proposed method is reminiscent of a simple study I described at stats.stackexchange.com/a/157646/919 in which I selected one sample from a set of random samples according to a simple "innocent" criterion. I think your method might be subject to the same criticisms as the one I examined: namely, it would have a bias that is unknown and potentially huge. How would you cope with that problem? $\endgroup$ – whuber Nov 27 '18 at 17:07

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