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

机译:阿根廷妇女的乳腺癌和可改变的生活方式因素:在病例对照研究中解决缺失数据的问题

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摘要

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 Córdoba (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.
机译:大量研究证明,饮食,母乳喂养和营养状况等可改变的生活方式因素对乳腺癌风险的影响。然而,没有人能够解决南美地区营养流行病学研究中的缺失数据问题。总体而言,数据丢失是乳腺癌研究和流行病学研究中的常见问题。如果没有采用适当的方法来处理丢失的数据,则从这些研究中获得的效果估计可能会有偏差。我们在科尔多瓦(阿根廷)的乳腺癌病例对照研究中对协变量的缺失值进行了多次插补,以优化风险估计。数据来自2008年至2015年的乳腺癌病例对照研究(318例,526例对照)。完整的病例分析和使用链式方程式的多重估算是用于评估传统饮食方式和与乳腺癌相关的其他公认因素的影响的方法。进行体育锻炼和社会经济状况的估算。进行逻辑回归模型。当进行完整的病例分析时,仅考虑了31%的女性。尽管两种方法均观察到传统饮食模式与乳腺癌呈正相关(完整病例分析OR = 1.3,95%CI = 1.0-1.7;多次插补OR = 1.4,95%CI = 1.2-1.7),其他仅在考虑多重插补时才能确定BMI和母乳喂养等协变量。当适当地进行多次插补时,在这种阿根廷人群中,传统的饮食方式,BMI和母乳喂养与乳腺癌的发生有关。拉丁美洲的流行病学研究建议采用多重插补,以优化将来的效果估算。

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