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Overview
Welcome to the Measurement Error Webinar Series, organized by collaborators from the National Cancer Institute, the NIH Office of Dietary Supplements, the United States Department of Agriculture, the Gertner Institute, Texas A&M University, and Wake Forest University. The series is intended for nutritionists, epidemiologists, statisticians, graduate students, and others with an interest in measurement error in dietary intake data. A basic level of familiarity with statistics and dietary assessment is recommended.
The goal of the webinar series is to provide participants with an understanding of:
- the sources and magnitudes of dietary measurement errors;
- how measurement error may affect estimates of usual dietary intake distributions;
- how measurement error may affect analyses of diet-health relationships; and
- how the effects of measurement error may be mitigated.
Concepts related to accounting for complex survey methods, estimating total intakes from diet and supplements, and the use of multiple dietary assessment instruments and self-report data along with biomarker data to reduce measurement error are also addressed.
An archive of the webinar series, which ran from September 20th to December 6th, 2011, is provided below. Session descriptions provide details on the topics covered and the objectives, recommended resources, and key terms for each webinar. Additional supporting materials, including a glossary, are available from the links at the right of this page.
Session Descriptions
Webinar 1: Introduction to the Problem of Measurement Error in Dietary Intake Data
Presenter: Sharon Kirkpatrick, National Cancer Institute
Session Objectives- Define concepts related to usual dietary intake.
- Identify random and systematic errors that may occur in dietary assessment and their impact on estimates of dietary intake.
- Describe statistical concepts underpinning approaches to correcting for measurement error in self-report dietary intake data.
- Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D, Tooze JA, Krebs-Smith SM. Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc. 2006;106(10):1640-50.
- NCI Usual Dietary Intakes web pages.
Introduction to the Problem of Measurement Error in Dietary Intake Data
Webinar 2: Estimating Usual Intake Distributions for Dietary Components Consumed Daily by Nearly All Persons
Presenter: Kevin Dodd, National Cancer Institute
Session Objectives- Identify considerations in estimating usual intakes of nutrients and foods consumed nearly daily by nearly all persons.
- Describe statistical modeling techniques and associated data requirements for estimating usual intake distributions for nutrients and foods consumed nearly daily by nearly all persons.
- Describe assumptions made in current approaches to estimating usual intake distributions and challenges in self-report dietary data not addressed by current statistical modeling techniques.
- Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D, Tooze JA, Krebs-Smith SM. Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc. 2006;106(10):1640-50.
- Tooze JA, Kipnis V, Buckman DW, Carroll RJ, Freedman LS, Guenther PM, Krebs-Smith SM, Subar AF, Dodd KW. A mixed-effects model approach for estimating the distribution of usual intake of nutrients: the NCI method. Stat Med.2010;29(27):2857-68.
Estimating Usual Intake Distributions for Dietary Components Consumed Daily by Nearly All Persons
Webinar 3: Estimating Usual Intake Distributions for Dietary Components Consumed Episodically
Presenter: Janet Tooze, Wake Forest University
Session Objectives- Define key concepts of food consumption related to usual intake estimation.
- Identify challenges in estimating usual intake for episodically-consumed dietary constituents.
- Explain statistical modeling approaches for estimating usual intake for episodically-consumed dietary constituents.
- Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D, Tooze JA, Krebs-Smith SM. Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc. 2006;106(10):1640-50.
- Tooze JA, Midthune D, Dodd KW, Freedman LS, Krebs-Smith SM, Subar AF, Guenther PM, Carroll RJ, Kipnis V. A new statistical method for estimating the usual intake of episodically consumed foods with application to their distribution. J Am Diet Assoc. 2006;106(10):1575-87.
Estimating Usual Intake Distributions for Dietary Components Consumed Episodically
Webinar 4: Accounting for Complex Survey Design in Modeling Usual Intake
Presenter: Kevin Dodd, National Cancer Institute
Session Objectives- Identify considerations in the analysis of dietary data collected as part of a complex survey, including stratification, clustering and weighting.
- Identify methods of variance estimation for complex survey samples and how these are incorporated into estimation of usual intake distributions.
- Korn EL, Graubard BI. Analysis of Health Surveys. New York, NY: John Wiley & Sons; 1999.
- Tooze JA, Kipnis V, Buckman DW, Carroll RJ, Freedman LS, Guenther PM, Krebs-Smith SM, Subar AF, Dodd KW. A mixed-effects model approach for estimating the distribution of usual intake of nutrients: the NCI method. Stat Med. 2010;29(27):2857-68.
Accounting for Complex Survey Design in Modeling Usual Intake
Webinar 5: Estimating Usual Total Nutrient Intake Distributions from Diet and Supplements
Presenter: Regan Bailey, Office of Dietary Supplements, National Institutes of Health
Session Objectives- Identify key challenges and considerations in combining dietary and supplement intake data.
- Explain statistical approaches to estimating total nutrient intakes.
- Describe assumptions and caveats of current techniques of estimating total nutrient intakes.
- Bailey RL, Dodd KW, Goldman JA, Gahche JJ, Dwyer JT, Moshfegh AJ, Sempos CT, Picciano MF. Estimation of total usual calcium and vitamin D intakes in the United States. J Nutr. 2010;140(4):817-22.
- Dodd KW, Guenther PM, Freedman LS, Subar AF, Kipnis V, Midthune D, Tooze JA, Krebs-Smith SM. Statistical methods for estimating usual intake of nutrients and foods: a review of the theory. J Am Diet Assoc. 2006;106(10):1640-50.
- Garriguet D. Combining nutrient intake from food/beverages and vitamin/mineral supplements. Health Rep. 2010;21(4):71-84.
- Park SY, Murphy SP, Wilkens LR, Yamamoto JF, Kolonel LN. Allowing for variations in multivitamin supplement composition improves nutrient intake estimates for epidemiologic studies. J Nutr. 2006;136(5):1359-64.
Estimating Usual Total Nutrient Intake Distributions from Diet and Supplements
Webinar 6: The Problem of Measurement Error When Examining Diet-Health Relationships
Presenter: Laurence Freedman, Gertner Institute
Session Objectives- Explain the types and magnitude of measurement error that occur in dietary data.
- Describe statistical models for evaluating diet-health relationships, including energy adjustment models.
- Describe the qualitative and quantitative impact of measurement error on studies of diet-health relationships.
- Freedman LS, Schatzkin A, Midthune D, Kipnis V. Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst. 2011;103(14):1086-92.
- Kipnis V, Freedman LS, Brown CC, Hartman A, Schatzkin A, Wacholder S. Interpretation of energy adjustment models for nutritional epidemiology. Am J Epidemiol. 1993;137:1376-80.
- Kipnis V, Subar AF, Midthune D, Freedman LS, Ballard-Barbash R, Troiano RP, Bingham S, Schoeller DA, Schatzkin A, Carroll RJ. Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol. 2003;158(1):14-21; discussion 22-6.
- Schatzkin A, Subar AF, Moore S, Park Y, Potischman N, Thompson FE, Leitzmann M, Hollenbeck A, Morrissey KG, Kipnis V. Observational epidemiologic studies of nutrition and cancer: the next generation (with better observation). Cancer Epidemiol Biomarkers Prev. 2009;18(4):1026-32.
- Thiébaut AC, Kipnis V, Schatzkin A, Freedman LS. The role of dietary measurement error in investigating the hypothesized link between dietary fat intake and breast cancer – a story with twists and turns. Cancer Invest. 2008;26(1):68-73.
- Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65(4 Suppl):1220S-1228S; discussion 1229S-1231S.
The Problem of Measurement Error When Examining Diet-Health Relationships
Webinar 7: Assessing Diet-Health Relationships with FFQ: Focus on Dietary Components Consumed Daily by Nearly All Persons
Presenter: Douglas Midthune, National Cancer Institute
Session Objectives- Identify challenges in estimating diet-health relationships caused by measurement error in dietary assessment, with a focus on components consumed nearly daily by nearly all persons.
- Describe approaches to correct estimated diet-health relationships for bias due to measurement error when diet is assessed by a food frequency questionnaire.
- Understand the role of calibration studies in assessing and correcting for measurement error in dietary instruments.
- Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Measurement error in nonlinear models: a modern perspective, 2nd edition. Boca Raton, FL: Chapman and Hall CRC Press; 2006. Chapter 4, Regression calibration.
- Freedman LS, Schatzkin A, Midthune D, Kipnis V. Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst. 2011;103(14):1086-92.
- Kipnis V, Subar AF, Midthune D, Freedman LS, Ballard-Barbash R, Troiano RP, Bingham S, Schoeller DA, Schatzkin A, Carroll RJ. Structure of dietary measurement error: results of the OPEN biomarker study. Am J Epidemiol. 2003;158(1):14-21; discussion 22-6.
- Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. Am J Epidemiol. 1990;132(4):734-45.
- Rosner B, Willett WC, Spiegelman D. Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Stat Med. 1989;8(9):1051-69; discussion 1071-3.
Assessing Diet-Health Relationships with FFQ: Focus on Dietary Components Consumed Daily by Nearly All Persons
Webinar 8: Assessing Diet-Health Relationships with FFQ: Focus on Episodically-Consumed Dietary Components
Presenter: Victor Kipnis, National Cancer Institute
Session Objectives- Review statistical risk models for evaluating diet-health relationships in nutritional epidemiology.
- Describe application of regression calibration to correct for FFQ measurement error using repeat short-term reference measurements in a substudy.
- With focus on episodically-consumed dietary components, describe application of a new methodology to carry out regression calibration in risk models with energy-adjusted dietary covariates.
- Carroll RJ, Ruppert D, Stefanski LA, Crainiceanu CM. Measurement error in nonlinear models: a modern perspective, 2nd edition. Boca Raton, FL: Chapman and Hall CRC Press; 2006. Chapter 4, Regression calibration.
- Freedman LS, Schatzkin A, Midthune D, Kipnis V. Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst. 2011;103(14):1086-92.
- Kipnis V, Freedman LS, Brown CC, Hartman A, Schatzkin A, Wacholder S. Interpretation of energy adjustment models for nutritional epidemiology. Am J Epidemiol. 1993;137:1376-80.
- Kipnis V, Midthune D, Buckman DW, Dodd KW, Guenther PM, Krebs-Smith SM, Subar AF, Tooze JA, Carroll RJ, Freedman LS. Modeling data with excess zeros and measurement error: application to evaluating relationships between episodically consumed foods and health outcomes. Biometrics. 2009;65(4):1003-10.
- Rosner B, Spiegelman D, Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. Am J Epidemiol. 1990;132(4):734-45.
- Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65(4 Suppl):1220S-1228S; discussion 1229S-1231S.
Assessing Diet-Health Relationships with FFQ: Focus on Episodically-Consumed Dietary Components
Webinar 9: Estimating Usual Intake Distributions for Multivariate Dietary Variables
Presenter: Raymond Carroll, Texas A&M University
Session Objectives- Identify challenges in addressing measurement error when modeling multivariate dietary variables such as diet quality indices.
- Describe statistical modeling techniques to correct for measurement error in estimating multivariate dietary variables.
- Guenther PM, Reedy J, Krebs-Smith SM, Reeve BB. Evaluation of the Healthy Eating Index-2005. J Am Diet Assoc. 2008;108(11):1854-64.
- Guenther PM, Reedy J, Krebs-Smith SM. Development of the Healthy Eating Index-2005. J Am Diet Assoc. 2008;108(11):1896-901.
- Reedy J, Mitrou PN, Krebs-Smith SM, Wirfält E, Flood A, Kipnis V, Leitzmann M, Mouw T, Hollenbeck A, Schatzkin A, Subar AF. Index-based dietary patterns and risk of colorectal cancer: the NIH-AARP Diet and Health Study. Am J Epidemiol. 2008;168(1):38-48.
- Zhang S, Krebs-Smith SM, Midthune D, Perez A, Buckman D, Kipnis V, Freedman LS, Dodd KW, Carroll RJ. Fitting a bivariate measurement error model for episodically consumed dietary components. Intl J Biostat. 2011;7(1):Article 1.
- Zhang S, Midthune D, Guenther PM, Krebs-Smith SM, Kipnis V, Dodd KW, Buckman DW, Tooze JA, Freedman L, Carroll RJ. A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment. Ann Appl Stat. 2011;5(2B):1456-87.
Estimating Usual Intake Distributions for Multivariate Dietary Variables
Webinar 10: Combining Self-Report Dietary Assessment Instruments to Reduce the Effects of Measurement Error
Presenter: Douglas Midthune, National Cancer Institute
Session Objectives- Describe methods of combining self-report dietary instruments to reduce measurement error and improve the power to detect diet-health associations.
- Understand situations in which combining information from various instruments may provide the most benefit.
- Carroll RJ, Midthune D, Subar AF, Shumakovich M, Freedman LS, Thompson FE, Kipnis V. Taking advantage of the strengths of two different dietary assessment instruments to improve intake estimates for nutritional epidemiology. Am J Epidemiol. 2012 Feb 15;175(4):340-7.
- Kipnis V, Midthune D, Buckman DW, Dodd KW, Guenther PM, Krebs-Smith SM, Subar AF, Tooze JA, Carroll RJ, Freedman LS. Modeling data with excess zeros and measurement error: application to evaluating relationships between episodically consumed foods and health outcomes. Biometrics. 2009;65(4):1003-10.
Combining Self-Report Dietary Assessment Instruments to Reduce the Effects of Measurement Error
Webinar 11: Combining Self-Report Dietary Intake & Biomarker Data to Reduce Measurement Error Effects
Presenter: Laurence Freedman, Gertner Institute
Session Objectives- To describe the motivation for combining dietary self-reports and biomarkers.
- To provide an overview of different methods of combining self-reports and biomarkers, their aims, and the knowledge required for implementing each method.
- To identify the potential gains of such combinations and the limitations of the methods.
- Freedman LS, Kipnis V, Schatzkin A, Tasevska N, Potischman N. Can we use biomarkers in combination with self-reports to strengthen the analysis of nutritional epidemiologic studies? Epidemiol Perspect Innov. 2010;7(1):2.
- Freedman LS, Midthune D, Carroll RJ, Tasevska N, Schatzkin A, Mares J, Tinker L, Potischman N, Kipnis V. Using regression calibration equations that combine self-reported intake and biomarker measures to obtain unbiased estimates and more powerful tests of dietary associations. Am J Epidemiol. 2011 Dec 1;174(11):1238-45.
- Freedman LS, Tasevska N, Kipnis V, Schatzkin A, Mares J, Tinker L, Potischman N. Gains in statistical power from using a dietary biomarker in combination with self-reported intake to strengthen the analysis of a diet disease association: an example from CAREDS. Am J Epidemiol. 2010;172(7):836-42.
Combining Self-Report Dietary Intake & Biomarker Data to Reduce Measurement Error Effects
Webinar 12: Assessing Diet-health Relationships Using a Short-term Unbiased Dietary Instrument: Focus on Risk Models with Multiple Dietary Components
Presenter: Victor Kipnis, National Cancer Institute
Session Objectives- Identify challenges in estimating relationships between a dietary exposure measured by repeated application of a short-term unbiased instrument and a health outcome in a risk model with multiple dietary components.
- Describe potential approaches to correct for within-person random measurement error in estimating relationships between a dietary exposure measured by a short-term unbiased instrument and a health outcome in a risk model with multiple dietary components.
- Kipnis V, Midthune D, Buckman DW, Dodd KW, Guenther PM, Krebs-Smith SM, Subar AF, Tooze JA, Carroll RJ, Freedman LS. Modeling data with excess zeros and measurement error: application to evaluating relationships between episodically consumed foods and health outcomes. Biometrics. 2009;65(4):1003-10.
- Zhang S, Midthune D, Guenther PM, Krebs-Smith SM, Kipnis V, Dodd KW, Buckman DW, Tooze JA, Freedman L, Carroll RJ. A new multivariate measurement error model with zero-inflated dietary data, and its application to dietary assessment. Ann Appl Stat. 2011;5(2B):1456-87.
Assessing Diet-health Relationships Using a Short-term Unbiased Dietary Instrument: Focus on Risk Models with Multiple Dietary Components
Dedication
