Use of three summary measures of pediatric vaccination for studying the safety of the childhood immunization schedule.
Collinearity
Immunization safety
Pediatric vaccination
Summary measures
Variance inflation factor
Journal
Vaccine
ISSN: 1873-2518
Titre abrégé: Vaccine
Pays: Netherlands
ID NLM: 8406899
Informations de publication
Date de publication:
28 02 2019
28 02 2019
Historique:
received:
17
07
2018
revised:
10
01
2019
accepted:
14
01
2019
pubmed:
3
2
2019
medline:
8
7
2020
entrez:
3
2
2019
Statut:
ppublish
Résumé
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. To evaluate the performance of multiple regression models using summary measures as risk factors to simulated binary outcomes. 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. 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 30709727
pii: S0264-410X(19)30104-5
doi: 10.1016/j.vaccine.2019.01.040
pmc: PMC6532405
mid: NIHMS1519408
pii:
doi:
Substances chimiques
Vaccines
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1325-1331Subventions
Organisme : NIAID NIH HHS
ID : R01 AI107721
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002535
Pays : United States
Informations de copyright
Copyright © 2019 Elsevier Ltd. All rights reserved.
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