Statistical Analysis
Continuous data are shown as means with standard deviation and categorical data as frequencies and percentages. Group comparisons were made using the chi squared test and t test or Kruskal-Wallis test for discrete and continuous variables, respectively, where appropriate. We used repeated measures analysis to perform univariate and multivariable regression to determine the outcome across the visits. While the primary endpoint for GOAL Canada (11) was the proportion of patients achieving the recommended LDL-C level, the purpose of this analysis was a comparison between specialists and PCPs with respect to any differences in the primary endpoint and the use of additional recommended lipid lowering therapies.
Multivariable logistic regression model was developed to assess factors independently associated with LDL-C achieving target of ≤2.0 mmol. The following variables were considered: variables in Table 1 with p<0.05 and specialist or PCP group. To account for the clustering of patients within visits, we performed a generalized estimating equations (GEE) model. The working correlation structure selected was based on its lowest quasi-likelihood under the independence model criterion (QIC). Adjusted odds ratio (OR) with 95% confidence intervals (CI) are presented. A value of P<0.05 was considered significant for all tests. All statistical analyses were performed in SAS software version 9.4 (SAS Institute, Cary, NC).