Repeated measures for two within-subject factors: analysis and missing data solutions.

The statistical analysis of the repeated measures design with two factors within and no factors between subjects, which is popular in clinical pharmacology, is discussed. Use of restricted maximum likelihood (REML) methodology is compared to an imputation procedure in small sample situations with missing data and is illustrated by simulations. While imputation leads to undesirable results that are not easily corrected, REML estimation in which test statistics are compared to an F-distribution provides an elegant tool for the analysis of these designs.