Repeated measures designs in behavioral toxicology: application to chronic marijuana smoke exposure

This paper discusses the application of repeated measures methods in the statistical analysis of an experiment in behavioral toxicology. The chronic marijuana smoke exposure study conducted at the National Center for Toxicological Research is used for an example of the types of problems that one encounters in analyzing these types of studies. In particular, the standard univariate analysis most frequently used for repeated measures analyses has some very restrictive assumptions on the form of the covariance matrices. These assumptions are not met in the example discussed and are rarely met in many other problems. Other possible models for analyzing repeated measures when these assumptions are not met are presented and discussed. Other problems specific to the chronic marijuana smoke exposure study that may occur in similar type studies are presented. These include pooling the experimental units into groups with comparable baselines, choosing a function of the measures to be analyzed, dealing with a large data set with many observation times and missing data, unequal group sizes and different designs for different subsets of the experimental animals. The standard univariate repeated measures analysis was chosen to analyze the data even though the violations of the covariance assumptions may lead to finding differences that do not exist (Type I or false-positive errors), since the other methods presented also had covariance assumptions that were not met or had low power. Use of Bonferroni-type multiple comparisons on the single degree of freedom contrasts of interest hopefully reduced the chances of these false-positive results.