Concern about potential imbalance on risk factors in community intervention trials often prompts researchers to adopt a pair-matched design in which similar clusters of individuals are paired and one member of each matched pair is then randomly assigned to the intervention group. It is known that if there are few clusters in trial, it becomes increasingly difficult to obtain close matches on all potential risk factors. One may thus offset any gain in precision with loss in degrees of freedom due to matching. We shown in this paper that there are also several analytic limitations with pair-matched designs. These include: the restriction of prediction models to cluster-level baseline risk factors (for example, cluster size), the inability to test for homogeneity of odds ratios, and difficulties in estimating the intracluster correlation coefficient. These limitations lead us to present arguments that favour stratified designs in which there are more than two clusters in each stratum.