John VanBuren1, Joseph Cavanaugh2, Teresa Marshall3, John Warren3, Steven M Levy3. 1. Pediatrics - Division of Critical Care, University of Utah, Salt Lake City, UT, USA. 2. Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, USA. 3. Preventative & Community Dentistry, University of Iowa, Iowa City, IA, USA.
Abstract
OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: Teresa A Marshall; Julie M Eichenberger-Gilmore; Michelle A Larson; John J Warren; Steven M Levy Journal: J Am Dent Assoc Date: 2007-01 Impact factor: 3.634
Authors: C A Palmer; R Kent; C Y Loo; C V Hughes; E Stutius; N Pradhan; M Dahlan; E Kanasi; S S Arevalo Vasquez; A C R Tanner Journal: J Dent Res Date: 2010-09-21 Impact factor: 6.116
Authors: E Whitney Evans; Catherine Hayes; Carole A Palmer; Odilia I Bermudez; Steven A Cohen; Aviva Must Journal: J Acad Nutr Diet Date: 2013-05-23 Impact factor: 4.910
Authors: Teresa A Marshall; Steven M Levy; Barbara Broffitt; John J Warren; Julie M Eichenberger-Gilmore; Trudy L Burns; Phyllis J Stumbo Journal: Pediatrics Date: 2003-09 Impact factor: 7.124
Authors: Alexandra M Curtis; John VanBuren; Joseph E Cavanaugh; John J Warren; Teresa A Marshall; Steven M Levy Journal: J Public Health Dent Date: 2018-05-12 Impact factor: 1.821
Authors: John J Warren; John M Van Buren; Steven M Levy; Teresa A Marshall; Joseph E Cavanaugh; Alexandra M Curtis; Justine L Kolker; Karin Weber-Gasparoni Journal: Community Dent Oral Epidemiol Date: 2017-07-03 Impact factor: 3.383
Authors: Breanne N Wright; Janet A Tooze; Regan L Bailey; Yibin Liu; Rebecca L Rivera; Lacey McCormack; Suzanne Stluka; Lisa Franzen-Castle; Becky Henne; Donna Mehrle; Dan Remley; Heather A Eicher-Miller Journal: J Acad Nutr Diet Date: 2020-07-20 Impact factor: 4.910