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Use of three summary measures of pediatric vaccination for studying the safety of the childhood immunization schedule
Stanley Xu1, Sophia R Newcomer2, Martin Kulldorff3
1Institute for Health Research, Kaiser Permanente Colorado, United States; University of Colorado Denver, School of Public Health, United States.
Insights
High correlation among vaccine summary measures, like antigen number and aluminum exposure, does not prevent their use in regression models. Accurate risk factor specification is key for reliable results in vaccine safety research.
Area of Science:
- Epidemiology
- Biostatistics
- Vaccine Safety Research
Background:
- Parents express concerns regarding the number of vaccines and aluminum exposure in young children.
- Summary measures of vaccine exposure are highly correlated, potentially complicating statistical analyses.
Purpose of the Study:
- To evaluate the performance of multiple regression models using vaccine summary measures as risk factors.
- To assess the impact of high correlation and redundant factors on detecting associations with simulated outcomes.
Main Methods:
- Calculated vaccine summary measures for a large cohort of over 230,000 children.
- Assessed correlations and variance inflation factors (VIFs) among summary measures.
- Conducted simulations to test regression model performance under various scenarios, including redundant risk factors and incorrect relationships.
Main Results:
- Summary measures for vaccine antigens, number of vaccines, and aluminum exposure were highly correlated.
- Simulations showed that associations could be detected even with non-true risk factors, but power decreased with redundant factors.
- Multiple regression models were suitable for identifying stronger risk factors when all measures were included, emphasizing the importance of correct relationship specification.
Conclusions:
- Multiple regression models can effectively analyze vaccine summary measures despite high inter-correlations.
- Accurate specification of the relationship between risk factors and outcomes is critical for valid vaccine safety assessments.
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.
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