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Breast Cancer and Modifiable Lifestyle Factors in Argentinean Women: Addressing Missing Data in a Case-Control Study

  • Coquet, Julia Becaria (Instituto de Investigaciones en Ciencias de la Salud (INICSA-UNC-CONICET), Universidad Nacional de Cordoba (UNC)) ;
  • Tumas, Natalia (Centro de Investigaciones y Estudio sobre Cultura y Sociedad (CIECS), Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Ciudad Universitaria) ;
  • Osella, Alberto Ruben (Laboratorio di Epidemiologia e Biostatistica, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS) Saverio de Bellis) ;
  • Tanzi, Matteo (Laboratorio di Epidemiologia e Biostatistica, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS) Saverio de Bellis) ;
  • Franco, Isabella (Laboratorio di Epidemiologia e Biostatistica, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS) Saverio de Bellis) ;
  • Diaz, Maria Del Pilar (Biostatistics Unit. School of Nutrition, Faculty of Medical Sciences, University of Cordoba)
  • Published : 2016.10.01

Abstract

A number of studies have evidenced the effect of modifiable lifestyle factors such as diet, breastfeeding and nutritional status on breast cancer risk. However, none have addressed the missing data problem in nutritional epidemiologic research in South America. Missing data is a frequent problem in breast cancer studies and epidemiological settings in general. Estimates of effect obtained from these studies may be biased, if no appropriate method for handling missing data is applied. We performed Multiple Imputation for missing values on covariates in a breast cancer case-control study of $C{\acute{o}}rdoba$ (Argentina) to optimize risk estimates. Data was obtained from a breast cancer case control study from 2008 to 2015 (318 cases, 526 controls). Complete case analysis and multiple imputation using chained equations were the methods applied to estimate the effects of a Traditional dietary pattern and other recognized factors associated with breast cancer. Physical activity and socioeconomic status were imputed. Logistic regression models were performed. When complete case analysis was performed only 31% of women were considered. Although a positive association of Traditional dietary pattern and breast cancer was observed from both approaches (complete case analysis OR=1.3, 95%CI=1.0-1.7; multiple imputation OR=1.4, 95%CI=1.2-1.7), effects of other covariates, like BMI and breastfeeding, were only identified when multiple imputation was considered. A Traditional dietary pattern, BMI and breastfeeding are associated with the occurrence of breast cancer in this Argentinean population when multiple imputation is appropriately performed. Multiple Imputation is suggested in Latin America's epidemiologic studies to optimize effect estimates in the future.

Keywords

Body mass index;breastfeeding;cancer epidemiology;dietary pattern;multiple imputation

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