To assess the extent to which propensity matching reduced confounders, the distribution of several variables were compared before and after matching, taking into consideration sex, age, race, smoking status, time in the database, concomitant diagnoses, preventive screenings (Pap or prostate-specific antigen test), concomitant medications, and calendar time. Unmatched group comparisons were made using t tests for independent samples and chi-square tests. Matched group comparisons were made using paired t tests and McNemar tests. Conditional Cox regression was used to account for the matched pairs to estimate the association of statin use with cancer. In the multivariable Cox regression, there was adjustment for propensity score, as well as other covariates, to control for any residual confounding; however, the propensity score was nonsignificant in the final model (6). Covariates included in the Cox regression model were all variables included in the propensity model (including flags for imputed BMI and VLDL); concomitant diagnoses (e.g., chronic obstructive pulmonary disease) before time zero; use of various medications (tumor necrosis factor alpha inhibitors, immunosuppressants, glucocorticoids, omega-3, acetylsalicylic acid, hormones, or nonsteroidal anti-inflammatory drugs) before time zero; and metabolic measurements (LDL, VLDL, total cholesterol, triglycerides, BMI). In addition, the definition of a diagnosis of a condition before time zero was adjusted to include persons on condition-specific medication for diabetes, hypertension, peripheral vascular disease, and ischemic heart disease. For example, the use of sulfonylureas or insulin was used as a surrogate for a diagnosis of diabetes mellitus. Interactions were formally tested by entering interaction terms between statin use and the main predictor variables, adjusting for propensity to use statins. Only 2 interaction terms were statistically significant: acetylsalicylic acid-statins and hormones-statins. However, these 2 interaction terms did not substantially alter the findings; therefore, no interaction terms were included in the final model. All data analyses were performed using SAS version 9.1 (proc logistic for generating propensity scores, proc lifetest for generating Kaplan-Meier curves, proc SQL for generating plots, and proc freq, proc means, proc ttest, and proc sort for descriptive statistics and significance testing).