University of Washington - Department of Statistics
In longitudinal studies, the usual modeling assumptions for multivariate analyses don't always hold up so well. One way to treat this is to use non-parametric approaches. In the paper I will be presenting on, the authors analyzed tumor volume in rats as a function of lipids in their diet. The data was highly heteroscedastic and strongly correlated with time. To compare lipid diets, randomization F-tests were used. Then, local polynomial smoothing was used to create tumor growth curves for each diet, as well as confidence intervals that account for the serially correlated data. While no significant difference in diets is obtained, this paper is useful in illustrating a process by which non-parametric methods can be used to deal with assumption problems created by longitudinal data.
Paper: A Non-Parametric Regression Approach to Repeated Measures Analysis in Cancer Experiments
Authors: Villa, Cabral, Escriche, Solanas
Source: Journal of Applied Statistics (1999)