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ANALYTICAL METHODS in NUTRITIONAL EPIDEMIOLOGY NUTR 818-Fall 2009 Mondays 9:00 -10:30 Tuesdays 5:00-6:30 in the Micro Computer Lab

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ANALYTICAL METHODS in NUTRITIONAL EPIDEMIOLOGY NUTR 818-Fall 2009 Mondays 9:00 -10:30 Tuesdays 5:00-6:30 in the Micro Computer Lab 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 [email protected] E-mail: [email protected] Course Description: This course will blend lecture/discussion and “hands on” laboratory assignments as a means to learn 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. 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. 1 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) 2 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. 3 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.
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