LAUSR

BACKGROUND Summary measures such as number of vaccine antigens, number of vaccines, and vaccine aluminum exposure by the 2nd birth day are directly related to parents' concerns that children receive too many vaccines over a brief period. High correlation among summary measures could cause problems in regression models that examine their associations with outcomes. OBJECTIVES To evaluate the performance of multiple regression models using summary measures as risk factors to simulated binary outcomes. METHODS We calculated summary measures for a cohort of 232,627 children born between 1/1/2003 and 9/31/2013. Correlation and variance inflation factors (VIFs) were calculated. We conducted simulations (1) to examine the extent to which an association can be detected using a summary measure other than the true risk factor; (2) to evaluate the performance of multiple regression models including true and redundant risk factors; (3) to evaluate the performance of multiple regression models when all three were risk factors; (4) to examine the performance of multiple regression models with incorrect relationship between risk factors and outcome. RESULTS These summary measures were highly correlated. VIFs were 7.14, 6.25 and 2.17 for number of vaccine antigens, number of vaccines, and vaccine aluminum exposure, respectively. In simulations, an association would be detected if a summary measure other than the true risk factor was used. The power to detect the association between the true risk factor and outcome significantly decreased if redundant risk factors were included. When all three were risk factors, multiple regression model was appropriate to detect the stronger risk factor. Correctly specifying the relationship between risk factors and the outcome was crucial. CONCLUSIONS Multiple regression models can be used to examine the association between summary measures and outcome despite of high correlation among summary measures. It is important to correctly specify the relationship between risk factors and outcome.