  One way to investigate the precision of estimates likely to result from
planned experiments and planned epidemiological studies is to simulate a large
number of possible outcomes and analyse the sets of possible results. This
appears to be computationally expensive for some multi-stage designs, so choice
of designs is instead based on theoretical derivation of expected information.
This paper shows that for some types of studies the analysis of large numbers
of simulated outcomes can be achieved more rapidly by making use of
interpolation.
