ANALYTICAL METHODS IN NUTRITIONAL EPIDEMIOLOGY 
                                                                                                   NUTR 818-Fall 2009 
                                                                                                   Mondays 9:00 -10:30  
                                                                                 Tuesdays 5:00-6:30 in the Micro computer lab 
                       
                      Linda Adair, Ph.D.      Anna Maria Siega-Riz 
                      405 University Square      205I University Square East  
                                                                                                                                                                   
                      Phone: 966-4449      E-mail  am_siegariz@unc.edu
                                                                    
                      E-mail: linda_adair@unc.edu
                       
                                                        This course will blend lecture/discussion and “hands on” laboratory assignments as a means to learn 
                      Course Description:
                      about analytic methods in nutrition epidemiology. Students will gain basic proficiency in the methods through conducting 
                      statistical analysis using nutrition data selected for task. 
                       
                      Topics to be covered include:  
                       
                      I.            DATA:  Basics of data management, data analysis using Stata 
                                                 Protecting confidentiality of data: human subjects issues, deductive disclosure 
                       
                      II.           DIET: From assessment of intake to diet-disease analysis 
                                                 Selection, creation, and validation of dietary assessment tools  
                                                 Use of food composition tables to calculate nutrient intakes 
                                                 Combining data from 24 hour recalls with FFQ to improve nutrient estimates 
                                                 Defining nutrition exposures: timing of measurement 
                                                 Measurement error, over and under-reporting of dietary intake   
                                                 Energy adjustment methods 
                                                 Use of biomarkers 
                       
                      III.          ANTHROPOMETRY and BODY COMPOSITION 
                                                 What is measured and why?   
                                                 Validation of anthropometric data 
                                                 Use of reference data: selection of reference data, calculation of Z-scores 
                                                 CDC and WHO growth charts, IOTF reference data, cut points and definitions of overweight and obesity 
                                                   
                      IV.           GENETICS and GENE-ENVIRONMENT INTERACTIONS IN NUTRITION EPIDEMIOLOGY  
                       
                      V.            PHYSICAL ACTIVITY: MEASUREMENT AND ANALYSIS OF PHYSICAL ACTIVITY DATA 
                       
                      VI.           APPLICATION OF METHODS RELEVANT TO NUTRITION EPIDEMIOLOGY 
                                                 Elucidating pathways: translating conceptual models into statistical models and dealing with confounders, 
                                                 mediators, endogeneity, multilevel models 
                                                 Working with large samples; design effects, use of sample weights etc. 
                                                 Sample selectivity: Loss to follow-up, bias, generalizability 
                                                 Longitudinal analysis 
                       
                      At the end of this course the student should be able to:1)select the appropriate diet assessment tool including 
                      designing/updating a food frequency questionnaire and apply the appropriate statistical method in the analysis; 2) 
                      appropriately categorize nutritional exposures; 3) calculate z scores for anthropometric data and understand their use in 
                      analysis 4) apply statistical techniques for sample design effects; 5) understand the basics of how to build statistical models, 
                      6) analyze and interpret environment-gene interactions, 7) select appropriate physical activity indicators for epidemiologic 
                      studies and 8) assess generalizability and bias related to sample selectivity and loss to follow-up.  
                       
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                      Requirements and Grading
                       
                      Students are expected to attend all classes and labs, read assigned materials, and participate in class discussions. Most of the 
                      assigned reading materials will be available in electronic form and will be posted on the Blackboard site for the course.   
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      Students may find Willet’s Nutritional Epidemiology text book, second edition, 1998 to be useful as a background 
      reference.  
       
      Written assignments:   
       
      1.  Labs: Labs will be started in class, and then students will complete the assignment and analysis and write up their 
      results. Instructions, questions and data sets will be provided for each lab. Students may work together to discuss concepts 
      and methods, but individuals must do their own write-ups. 
       
      2.  Paper/presentation: Each student will select a topic related to one of the methods discussed in the class. Students 
      should ask and then answer a specific methodological question by conducting data analysis.  The main topic/question and 
      data to be used for the analysis should be selected in consultation with and approved in advance by the course instructors.  
      Students are expected to meet with course instructors to discuss their ongoing analysis. Results should be written up in a 
      paper (suggested length not to exceed 10 pages of text, double spaced), and presented to the class in a 10 minute talk.  
      Where applicable, students are responsible for obtaining IRB approval for their projects.  
       
      Papers and presentations should be organized as follows: 
       
      1. Background: what motivates the research question? why is it important? what will you contribute? 
      2. Statement of purpose/aim/research question: 1 sentence statement of main objective 
      3. Methods 
          a. description of sample and key variables 
          b. analytic methods 
      4. Results 
      5. Discussion 
       
      Due Dates: 
      September 8:  Identify data set and main question:  Please submit 1-2 paragraphs describing: (1) the main issue you will 
      address and why it is important; (2) the data set you will use.  Please fill out the table at the end of this syllabus to provide 
      the information about your data. 
      September 21: Progress report: Brief (1 paragraph description of what you have done, and questions you may have about 
      how to proceed, etc.)  
      December 7, 8  Presentations 
      Dec 14: Final papers due 
       
      Unless otherwise noted on the syllabus, LABS will be due one week after the lab session.  
       
      Grades will be based on: labs (50%), class participation (10%) and the final project (40%: 30% written, 10% oral) 
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             Dates and Topics 
              
             T Aug 25 Introduction and confidentiality, data protections, management, creation of data files, and proper 
                                    documentation (lab/demo) (Adair) 
              
                      A useful overview/review of basic issues in nutritional epidemiology: 
                       
                       Sempos CT, Liu K, Ernst ND. Food and nutrient exposures: what to consider when evaluating 
                      epidemiologic evidence. Am J Clin Nutr. 1999 Jun;69(6):1330S-1338S. sempos1.pdf 
                       
                      Guidelines related to data security, need for IRB review. 
                      datasecurity.pdf 
                      student_research_irb_guidance.pdf 
                      deidentified data.pdf 
              
             M Aug 31 Anthropometry, body composition, and controversies relate to the use of reference data and 
                                    cutpoints  (Adair) 
              
                      Sources for growth reference data: 
                       
                      CDC/NCHS 2000: http://www.cdc.gov/growthcharts/ 
                      WHO:  http://www.who.int/childgrowth/mgrs/en/ 
                       
                      IOTF: Cole TJ, Bellizzi MC, Flegal K. Establishing a standard definition for child overweight and obesity 
                      worldwide: international survey. BMJ 2000;320:1240-1243 ( 6 May ) ColeIOTF.pdf 
                       
                      Cole TJ, Flegal KM, Nicholls D, Jackson AA. Body mass index cut offs to define thinness in children and 
                      adolescents: international survey. BMJ. 2007 Jul 28;335(7612):194. Coleundernutrition.pdf 
                       
                      Comparison of results from new and old reference data: 
                      de Onis M, Garza C, Onyango AW, Borghi E. Comparison of the WHO child growth standards and the 
                      CDC 2000 growth charts. J Nutr. 2007 Jan;137(1):144-8 deOnis.pdf 
                       
                      Representing BMI in adolescents:  
                      Berkey CS, Colditz GA. Adiposity in adolescents: change in actual BMI works better than change in BMI 
                      z score for longitudinal studies. Ann Epidemiol. 2007 Jan;17(1):44-50. berkey&colditz.pdf 
                       
                      The debate about cut points: 
                      Razak F, Anand SS, Shannon H, Vuksan V, Davis B, Jacobs R, Teo KK, McQueen M, Yusuf S. 
                      Defining obesity cut points in a multiethnic population. Circulation. 2007 Apr 24;115(16):2111-8. 
                      Razak.pdf 
                                      
             T Sept 1 Lab #1  Anthropometry Lab (Adair)  
              
             M       Sept 7   LABOR DAY: No Class 
              
             T Sept 8 Diet Assessment –new tools and methodological techniques (Siega-Riz)   
              
                      Automated Self-Administered 24 hour dietary recall (ASA24) 
                      http://riskfactor.cancer.gov/tools/instruments/asa24/ 
                       
                      Subar AF, Dodd KW, Guenther PM, Kipnis V, Midthune D, McDowell M, Tooze JA, Freedman LS, 
                      Krebs-Smith SM. The food propensity questionnaire: concept, development, and validation for use as a 
                      covariate in a model to estimate usual food intake. J Am Diet Assoc. 2006 Oct;106(10):1556-63. 
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           SubarFPQ.pdf 
       
       
      M Sep 14 Estimating Usual Intake and Identifying Outliers (Siega-Riz) 
       
           Dodd K, Guenther P, et al. Statistical methods for estimating usual intake of nutrients and foods: A review 
           of the Theory. JADA 2006;106:1640-1650. Dodd_2006JADA_usualintakes.pdf 
            
           Tooze J, Midthune D, Dodd K, et al. A new statistical method for estimating the usual intake of 
           episodically consumed foods with application to their distribution.  JADA 2006;106:1575-87. Tooze.pdf 
            
           Kipnis V, Midthune D, Buckman, DW, et al.  Modeling data with excess zeros and measurement 
           error:Application to evaluating relationships between episodically consumed foods and health outcomes. 
           Biometrics 2009  Kipnis_biometrics_2009.pdf 
            
           Huang TTK  Effect of Screening Out Implausible Energy Intake Reports on Relationships between Diet 
           and BMI. Obesity Research 2005; 13:1205-17. Huang2005.pdf  
       
      T   Sep 15   Lab # 2: Assessing over and under-reporting of dietary intake and maybe usual intakes (Siega-Riz) 
       
           Lissner L, Troiano RP, Midthune D, Heitmann BL, Kipnis V, Subar AF, Potischman N. OPEN about obesity: 
           recovery biomarkers, dietary reporting errors and BMI. Int J Obes (Lond). 2007 Jun;31(6):956-61. Lissner.pdf 
            
           Huang TTK  Effect of Screening Out Implausible Energy Intake Reports on Relationships between Diet and 
           BMI. Obesity Research 2005; 13:1205-17. Huang2005.pdf 
       
      M   Sep 21  Discussion of projects: Turn in your progress report, and we will discuss ideas, approaches, etc. (Adair) 
       
      T Sep 22 Lab # 3 Energy adjustment  (Siega-Riz) 
       
           Hu. FB, et al.  Dietary fat and coronary heart disease: A comparison of approaches for adjusting for total 
           energy intake and modeling repeated dietary measurements.  Am J Epidemiol 1999;149:531-40.  
           Hu Dietary Fat.pdf  
            
           Bellach B, Kohlmeier L.  Energy Adjustment Does Not Control for Differential Recall Bias in Nutritional 
           Epidemiology.  J Clin Epid 1998; 51:393-398.  Bellach.pdf 
       
      M Sep 28 Representing data to test hypotheses: continuous, categorical, clusters, factors, etc. (Adair)  
       
           Note: In this paper, please focus on the ways in which the dietary data were categorized for the analysis,   
           in particular the use of categories of intake and quintiles. Think about the statistical methods that must be 
           used to deal with the different categorical variables, and how the categorization might affect the results.  
           Sempos CT, Flegal KM, Johnson CL et al. Issues in the long-term evaluation of diet in longitudinal studies 
           J Nutr 1993;123:406-12. sempos2.pdf 
           Dietary patterns: overview of the issues 
           Moeller SM, Reedy J, Millen AE, Dixon LB, Newby PK, Tucker KL, Krebs-Smith SM, Guenther PM. 
           Dietary patterns: challenges and opportunities in dietary patterns research an Experimental Biology 
           workshop, April 1, 2006. J Am Diet Assoc. 2007 Jul;107(7):1233-9. MoellerDietaryPatterns.pdf 
           Comparison of factor and cluster results     
           Newby PK, Muller D, Tucker KL.  Associations of empirically derived eating patterns with plasma lipid 
           biomarkers: a comparison of factor and cluster analysis methods. Am J Clin Nutr. 2004 Sep;80(3):759-67 
           Newby.pdf 
           Reduced rank regression: description of the method 
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