Multivariate analysis and repeated measurements: A primer

Most research designs in critical care medicine inherently are multivariate in that experimental manipulations are expected to produce changes on several dependent variables, including measurements repeated over time. Increased availability of easy to use computer programs has made it practical for investigators to benefit from the advantages of multivariate analyses, such as increased control of experiment-wise type I error rates and enhanced interpretation of treatment effects. Researchers need not understand the mathematical underpinnings of these analytic techniques to make good use of them, and it is the practical application of these statistical methods that is addressed in the present paper. The assumptions necessary for appropriate use of multivariate approaches, as well as discussion of the interpretations to be drawn from the information provided by computer programs, are presented. The multivariate analysis of variance (MANOVA) is discussed as an extension of its univariate counterpart, the analysis of variance (ANOVA), with the added advantage of assessing differential treatment effects simultaneously across multiple dependent measurements. Discriminant analysis, including examples of how to interpret discriminant weights and canonical loadings, is presented as a particularly useful method of providing an in-depth and unique representation of the results of a significant MANOVA. Finally, the utility of MANOVA is extended to repeated measurements experimental designs, including how to analyze and interpret designs that involve both between-subjects and within-subjects factors.