Rerandomization tests for analyzing correlated data from dental studies

Dental research studies often produce relatively small data sets in which observations are serially or spatially correlated. Rerandomization tests are presented as alternatives to analysis of variance and multivariate analysis for assessing group differences using such data. Rerandomization tests are particularly useful when the investigator is unwilling to make strong assumptions about the nature of the serial correlation or the distribution of the data. Two examples are discussed that demonstrate these techniques.