Descriptive statistics will be computed for all variables to ensure data quality and to evaluate the assumptions of statistical tests. Variable distributions will be described with histograms, box plots, central tendencies, measures of variation and frequency distributions. We will test the assumption of normality for the outcome measures, within the PWP and control groups. Additionally, we will test the assumption that the PWP and control groups have equal variances. Generalized linear mixed models (GLMMs) with unequal variances (McCulloch et al., 2008) can be applied when the ideal conditions of normality and equal variances are not met. Testing for the equality of variances is available with the COVTEST statement, which is based on the ratio of residual likelihoods or pseudo-likelihoods. Residuals at each level of the model will be examined to assess model fit. Generalized linear mixed models using SAS PROC GLIMMIX also provides the flexibility to model different types of outcome variables (binary, count and continuous data). All analyses will be conducted using SAS/STAT® software for Windows version 9.2 or higher.