Correcting for measurement error in nutritional epidemiology Ruth Keogh MRC Biostatistics Unit MRC Centre for Nutritional Epidemiology in Cancer Prevention.

Slides:

Advertisements

Similar presentations

Measuring Dietary Intake Raymond J. Carroll Department of Statistics Faculty of Nutrition and Faculty of Toxicology Texas A&M University

Advertisements

REGRESSION, IV, MATCHING Treatment effect Boualem RABTA Center for World Food Studies (SOW-VU) Vrije Universiteit - Amsterdam.

Implications of Total Energy Intake for Epidemiologic Analyses Chapter 11.

Comparing methods for addressing limits of detection in environmental epidemiology Roni Kobrosly, PhD, MPH Department of Preventive Medicine Icahn School.

Meat and Cancer: Assessment of Dietary Exposure to Heterocyclic Amines and Polycyclic Aromatic Hydrocarbons Nicole Cardello Deziel, PhD, MHS April 8, 2008.

Observational Designs Oncology Journal Club April 26, 2002.

Nancy R. Cook, ScD Championing Public Health Nutrition November 25-26, 2014 Sodium and Cardiovascular Health.

Bias in Clinical Research: Measurement Bias

In experiments the controlled variable is the variable which the experimenter has control of. It is also called the independent variable The dependant.

Chance, bias and confounding

Is a subgroup assessment sufficient? Department of Epidemiology German Institute of Human Nutrition Potsdam-Rehbrücke.

Nutrient and Physical Activity Biomarker Studies in the WHI 544 women completed two-week DLW protocol with urine and blood collection and with FFQ and.

Model and Variable Selections for Personalized Medicine Lu Tian (Northwestern University) Hajime Uno (Kitasato University) Tianxi Cai, Els Goetghebeur,

CHAPTER 3 ECONOMETRICS x x x x x Chapter 2: Estimating the parameters of a linear regression model. Y i = b 1 + b 2 X i + e i Using OLS Chapter 3: Testing.

Correlation: Relationships Can Be Deceiving. An outlier is a data point that does not fit the overall trend. Speculate on what influence outliers have.

Measuring Dietary Intake Raymond J. Carroll Department of Statistics Faculty of Nutrition and Toxicology Texas A&M University

Flexible modeling of dose-risk relationships with fractional polynomials Willi Sauerbrei Institut of Medical Biometry and Informatics University Medical.

Measures of association

SOME ADDITIONAL POINTS ON MEASUREMENT ERROR IN EPIDEMIOLOGY Sholom May 28, 2011 Supplement to Prof. Carroll’s talk II.

Chapter 6 Reproducibility: duplicate measurements of the same individual in the same situation and time frame. Validity: comparison of questionnaire data.

Gene-Environment Case-Control Studies Raymond J. Carroll Department of Statistics Faculty of Nutrition Texas A&M University

Model Selection in Semiparametrics and Measurement Error Models Raymond J. Carroll Department of Statistics Faculty of Nutrition and Toxicology Texas A&M.

Measuring Dietary Intake Raymond J. Carroll Department of Statistics Faculty of Nutrition and Faculty of Toxicology Texas A&M University

A New Model for Dietary Intake Instruments Based on Self- Report and Biomarkers Raymond J. Carroll Texas A&M University (

Sample Size Determination

1 A MONTE CARLO EXPERIMENT In the previous slideshow, we saw that the error term is responsible for the variations of b 2 around its fixed component 

Standard error of estimate & Confidence interval.

09/15/05William Wu / MS meeting1 Measurement error and measurement model with an example in dietary data.

Lecture 8 Objective 20. Describe the elements of design of observational studies: case reports/series.

Associations of Red Meat, Fat, and Protein Intake With Distal Colorectal Cancer Risk 100/5/31 鄒季臻 Nutr Cancer August ; 62(6): 701–709.

Evidence-Based Medicine 4 More Knowledge and Skills for Critical Reading Karen E. Schetzina, MD, MPH.

Measurement Error.

Criteria for Assessment of Performance of Cancer Risk Prediction Models: Overview Ruth Pfeiffer Cancer Risk Prediction Workshop, May 21, 2004 Division.

Validity and Reproducibility of Exposure Measurements: The Method of Triads Edmond K. Kabagambe and Hannia Campos Harvard School of Public Health.

Metrological issues for food consumption data. Max Feinberg INRA.

Biostatistics Case Studies 2005 Peter D. Christenson Biostatistician Session 4: Taking Risks and Playing the Odds: OR vs.

TWO-STAGE CASE-CONTROL STUDIES USING EXPOSURE ESTIMATES FROM A GEOGRAPHICAL INFORMATION SYSTEM Jonas Björk 1 & Ulf Strömberg 2 1 Competence Center for.

Biostatistics Case Studies Peter D. Christenson Biostatistician Session 5: Analysis Issues in Large Observational Studies.

Cross-sectional study

Estimation Bias, Standard Error and Sampling Distribution Estimation Bias, Standard Error and Sampling Distribution Topic 9.

ECON ECON Health Economic Policy Lab Kem P. Krueger, Pharm.D., Ph.D. Anne Alexander, M.S., Ph.D. University of Wyoming.

Montecarlo Simulation LAB NOV ECON Montecarlo Simulations Monte Carlo simulation is a method of analysis based on artificially recreating.

Lecture 6 Objective 16. Describe the elements of design of observational studies: (current) cohort studies (longitudinal studies). Discuss the advantages.

NS 210 – Unit 3 Seminar Measuring Diets and Interview Techniques Jennifer Neily, MS, RD, CSSD, LD Registered/Licensed Dietitian Kaplan University.

Idames pilot study Dietary intake Domenico Palli Molecular and Nutritional Epidemiology Unit Cancer Research and Prevention Institute (ISPO) Florence Italy.

Assessing dietary intakes in food environment research: Implications for policy and practice SHARON KIRKPATRICK University of Waterloo JILL REEDY, KEVIN.

Food Frequency Questionnaires BY: Noura Al-Ghilan, Ohood Al-Mubarak, Sara Al-Babtain.

Various topics Petter Mostad Overview Epidemiology Study types / data types Econometrics Time series data More about sampling –Estimation.

Economic evaluation of health programmes Department of Epidemiology, Biostatistics and Occupational Health Class no. 19: Economic Evaluation using Patient-Level.

Case-control study Chihaya Koriyama August 17 (Lecture 1)

Status on statistical methods in dietary assessment and Multiple Source Method Heiner Boeing German Institute of Human Nutrition Potsdam- Rehbrücke Department.

1 Snow depth distribution Neumann et al. (2006). 2.

MBP1010 – Lecture 8: March 1, Odds Ratio/Relative Risk Logistic Regression Survival Analysis Reading: papers on OR and survival analysis (Resources)

Brian Cox Research Associate Professor: Cancer epidemiology and screening University of Otago Hugh Adam Cancer Epidemiology Unit Department of Preventive.

1 Validation of dietary assessment methods present Heather Ward, Natasha Powell, Sheila Bingham, and Kay Tee Khaw, University of Cambridge.

Sean Canavan David Hann Oregon State University The Presence of Measurement Error in Forestry.

Pilot study: feasibility and evaluation A-K. Illner 1, U. Harttig 1, G. Tognon 2, S. Salvini 2, P. Amiano 3, E. Bower 3, D. Engeset 4, T. Kaasik 5, H.

Heiner Boeing Department of Epidemiology

Understanding lack of validity: Bias

X Treatment population Control population 0 Examples: Drug vs. Placebo, Drugs vs. Surgery, New Tx vs. Standard Tx  Let X = decrease (–) in cholesterol.

Case Control study. An investigation that compares a group of people with a disease to a group of people without the disease. Used to identify and assess.

Analysis of Mismeasured Data David Yanez Department of Biostatistics University of Washington July 5, 2005 Biost/Stat 579.

NS 210 – Unit 3 Seminar Interview Techniques Leslie Young MS RD LDN.

Andrew Mente, on behalf of the PURE investigators

Andrew Mente, on behalf of the PURE investigators

Simultaneous Inferences and Other Regression Topics

Elements of a statistical test Statistical null hypotheses

Reena Oza-Frank, MS-MPH, RD: Emory University (EU)

Measuring the Wealth of Nations

Presentation transcript:

Correcting for measurement error in nutritional epidemiology Ruth Keogh MRC Biostatistics Unit MRC Centre for Nutritional Epidemiology in Cancer Prevention and Survival IPH Showcase, 14 February 2011

Measuring dietary intake Food frequency questionnaires (FFQ) Diet diaries/24 hour recalls Biomarkers There is no gold standard measurement UK dietary cohort consortium EPIC-Norfolk + 5 other cohorts Case-control studies nested within cohorts 4-7 day diaries, FFQs Colorectal cancer, breast cancer, prostate cancer What is the association between long term or usual dietary intake and disease risk?

Food frequency questionnaires (FFQ)

Diet diaries

Choice of instrument FFQs Designed to measure long term intake Inexpensive  used for large populations Subject to substantial measurement error Diet diaries Measure actual intake Very expensive to process More highly correlated with objective biomarkers of intake Still subject to error UK dietary cohort consortium One of only a small number of studies using diaries/24 hr recalls as the main instrument Interested in correcting for error in dietary diary measurements

Effects of measurement error 1.Biased associations – usually attenuated 2.Loss of power to detect associations 3.Can hide nonlinear association shapes

Effects of measurement error 1.Biased associations – usually attenuated 2.Loss of power to detect associations 3.Can hide nonlinear association shapes

Effects of measurement error 1.Biased associations – usually attenuated 2.Loss of power to detect associations 3.Can hide nonlinear association shapes

Correcting for measurement error True diet-disease association Estimating  when we can’t observe T i Linear regression calibration model

Fitting the model We need to understand the structure of the error in diet diary measurements R i Random error in diary measurements: Suppose we have another diary measurement for some people In fact we can show that

Fibre intake and colorectal cancer OR per 6g/day increase 95% CI Using FFQ Using diet diary Corrected for measurement error in diary Dahm CC, Keogh RH et al. Dietary Fiber and Colorectal Cancer Risk: A Nested Case–Control Study Using Food Diaries. JNCI 2010

However… We do not believe that diet diaries are subject only to random error Biomarker studies suggest error in diaries depends on true intake errors in replicate diaries are not independent We cannot estimate the extra parameters using repeat measurements to correct for error we need unbiased measurements, e.g. biomarker biomarkers not available for most nutrients Different assumptions about the type of error in R i can give very different corrected estimates

Challenges Measurement error can have severe effects on observed diet- disease associations What we assume about the form of the error can have strong effects on ‘corrected’ estimated associations Biomarkers can help us to understand the structure of the measurement error Also… We often want to adjust for other dietary variables, such as total energy intake All dietary variables in the model are measured with error…