Statistical Analysis
Data analysis was executed using R. Two-sided p-values with alpha=0.05
were used. Distributions of characteristics were tabulated using
percentages for categorical variables and means with standard deviations
for continuous variables. Six multivariable linear regressions were fit
for the six outcomes using all risk factors as independent variables.
Models estimated beta coefficients (β) and 95% confidence intervals
(CI) representing associations between risk factors and outcomes.
Linear regression assumptions were evaluated using plots and hypothesis
tests. QQplots verified the assumption of normality. To test for
heteroskedasticity, residual plots were generated along with a
non-constant variance test. There was strong evidence of
heteroskedasticity. To correct this, all outcomes employed a Box-Cox
transformation. Lack of multicollinearity was confirmed by estimating
variance inflation factors.