Journal article
AIC identifies optimal representation of longitudinal dietary variables
Journal of public health dentistry, Vol.77(4), pp.360-371
09/2017
DOI: 10.1111/jphd.12220
PMCID: PMC5694386
PMID: 28517028
Abstract
The Akaike Information Criterion (AIC) is a well-known tool for variable selection in multivariable modeling as well as a tool to help identify the optimal representation of explanatory variables. However, it has been discussed infrequently in the dental literature. The purpose of this paper is to demonstrate the use of AIC in determining the optimal representation of dietary variables in a longitudinal dental study. The Iowa Fluoride Study enrolled children at birth and dental examinations were conducted at ages 5, 9, 13, and 17. Decayed or filled surfaces (DFS) trend clusters were created based on age 13 DFS counts and age 13-17 DFS increments. Dietary intake data (water, milk, 100 percent-juice, and sugar sweetened beverages) were collected semiannually using a food frequency questionnaire. Multinomial logistic regression models were fit to predict DFS cluster membership (n=344). Multiple approaches could be used to represent the dietary data including averaging across all collected surveys or over different shorter time periods to capture age-specific trends or using the individual time points of dietary data. AIC helped identify the optimal representation. Averaging data for all four dietary variables for the whole period from age 9.0 to 17.0 provided a better representation in the multivariable full model (AIC=745.0) compared to other methods assessed in full models (AICs=750.6 for age 9 and 9-13 increment dietary measurements and AIC=762.3 for age 9, 13, and 17 individual measurements). The results illustrate that AIC can help researchers identify the optimal way to summarize information for inclusion in a statistical model. The method presented here can be used by researchers performing statistical modeling in dental research. This method provides an alternative approach for assessing the propriety of variable representation to significance-based procedures, which could potentially lead to improved research in the dental community.
Details
- Title: Subtitle
- AIC identifies optimal representation of longitudinal dietary variables
- Creators
- John VanBuren - Pediatrics - Division of Critical Care, University of Utah, Salt Lake City, UT, USAJoseph Cavanaugh - Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USATeresa Marshall - Preventative & Community Dentistry, University of Iowa, Iowa City, IA, USAJohn Warren - Preventative & Community Dentistry, University of Iowa, Iowa City, IA, USASteven M Levy - Preventative & Community Dentistry, University of Iowa, Iowa City, IA, USA
- Resource Type
- Journal article
- Publication Details
- Journal of public health dentistry, Vol.77(4), pp.360-371
- DOI
- 10.1111/jphd.12220
- PMID
- 28517028
- PMCID
- PMC5694386
- NLM abbreviation
- J Public Health Dent
- ISSN
- 0022-4006
- eISSN
- 1752-7325
- Publisher
- United States
- Grant note
- R01 DE012101 / NIDCR NIH HHS R03 DE023784 / NIDCR NIH HHS M01 RR000059 / NCRR NIH HHS R56 DE012101 / NIDCR NIH HHS R01 DE009551 / NIDCR NIH HHS
- Language
- English
- Date published
- 09/2017
- Academic Unit
- Statistics and Actuarial Science; Preventive and Community Dentistry; Epidemiology; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9983917793302771
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