Evaluating assumptions for least squares analysis using the general linear model: a guide for the pharmaceutical industry statistician.

Abstract

A review of graphical and test based methods for evaluating assumptions underlying the use of least squares analysis with the general linear model is presented along with some discussion of robustness. Alternative analyses are described for situations where there is evidence that the assumptions are not reasonable. Evaluation of the assumptions is illustrated through the use of an example from a clinical trial used for US registration purposes. It is recommended that: (1) most assumptions required for the least squares analysis of data using the general linear model can be judged using residuals graphically without the need for formal testing, (2) it is more important to normalize data or to use nonparametric methods when there is heterogeneous variance between treatment groups, and (3) nonparametric analyses can be used to demonstrate robustness of results and that it is best to specify these analyses prior to unblinding.