This article and the accompanying spreadsheets will be very useful additions to the researcher’s toolbox. The article details a series of scenarios/study designs for which minimisation is advisable in all cases, even for crossover designs based on order of interventions. This is sound advice and the points are well taken.

I am interested in comparing Will’s spreadsheets to existing freeware such as the Minim program. Minim permits up to four groups, different proportions of patients in each group, any number of prognostic factors and categories for each factor (subject to a total of 100 categories for all factors together), and different weights for each prognostic factor if required, so that some factors can be treated as more important to balance for than others. On the issue of weighting, I note that for the scenario in which characteristics for all participants are known before allocation, Will’s method assigns primary weighting to one factor and equal importance to secondary factors, whereas for allocating participants as they are recruited equal weighting is assigned to all factors. I am unsure what influence this difference would have on resulting estimates of effects if one wished to weight factors more finely. However, I have never been in possession of sufficient a priori information to decide fine-tuned weighting between multiple prognostic factors, and in practice equal importance is the default assumption, so I doubt there is any practical advantage of Minim in this regard. In any event, Will proposes a neat side-step of this potential problem which allows one to ‘double-weight’ a variable by including it twice with identical values.

Will’s method is an advance on Minim in that it may be applied in situations in which characteristics for all people to be allocated are known in advance, rather than solely for scenarios in which participants are allocated as they are recruited. A further advantage over Minim is the ability to include variables measured on a continuous scale, though the article notes that the influence on outcome is likely to be small.