17
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

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

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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 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.

          Related collections

          Most cited references44

          • Record: found
          • Abstract: found
          • Article: not found

          How can I deal with missing data in my study?

          Missing data in medical research is a common problem that has long been recognised by statisticians and medical researchers alike. In general, if the effect of missing data is not taken into account the results of the statistical analyses will be biased and the amount of variability in the data will not be correctly estimated. There are three main types of missing data pattern: Missing Completely At Random (MCAR), Missing At Random (MAR) and Not Missing At Random (NMAR). The type of missing data that a researcher has in their dataset determines the appropriate method to use in handling the missing data before a formal statistical analysis begins. The aim of this practice note is to describe these patterns of missing data and how they can occur, as well describing the methods of handling them. Simple and more complex methods are described, including the advantages and disadvantages of each method as well as their availability in routine software. It is good practice to perform a sensitivity analysis employing different missing data techniques in order to assess the robustness of the conclusions drawn from each approach.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Working With Missing Values

            Alan Acock (2005)
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Adiposity and cancer risk: new mechanistic insights from epidemiology.

              Excess body adiposity, commonly expressed as body mass index (BMI), is a risk factor for many common adult cancers. Over the past decade, epidemiological data have shown that adiposity-cancer risk associations are specific for gender, site, geographical population, histological subtype and molecular phenotype. The biological mechanisms underpinning these associations are incompletely understood but need to take account of the specificities observed in epidemiology to better inform future prevention strategies.
                Bookmark

                Author and article information

                Journal
                Asian Pac J Cancer Prev
                Asian Pac. J. Cancer Prev
                Asian Pacific Journal of Cancer Prevention : APJCP
                West Asia Organization for Cancer Prevention (Iran )
                1513-7368
                2476-762X
                2016
                : 17
                : 10
                : 4567-4575
                Affiliations
                [1 ] Instituto de Investigaciones en Ciencias de la Salud (INICSA-UNC-CONICET), Universidad Nacional de Cordoba (UNC), Cordoba, Argentina
                [2 ] Centro de Investigaciones y Estudio sobre Cultura y Sociedad (CIECS), Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET), Ciudad Universitaria, Cordoba Capital, Cordoba, Argentina
                [3 ] Laboratorio di Epidemiologia e Biostatistica, Istituto di Ricerca e Cura a Carattere Scientifico (IRCCS) Saverio de Bellis, Castellana Grotte, Bari, Italia
                [4 ] Biostatistics Unit. School of Nutrition, Faculty of Medical Sciences, University of Cordoba, Avenida Enrique Barros s/n, Ciudad Universitaria, CP 5,000, Cordoba, Argentina
                Author notes
                [* ] For Correspondence: pdiaz@ 123456fcm.unc.edu.ar
                Article
                APJCP-17-4567
                10.22034/APJCP.2016.17.10.4567
                5454599
                27892664
                930ce995-07ef-4395-b7af-7f2d52917426
                Copyright: © Asian Pacific Journal of Cancer Prevention

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

                History
                Categories
                Research Article

                body mass index,breastfeeding,cancer epidemiology,dietary pattern,multiple imputation

                Comments

                Comment on this article