1. Measuring Dietary Intake Raymond J. Carroll Department of Statistics Faculty of Nutrition and Faculty of Toxicology Texas A&M University http://stat.tamu.edu/~carroll _________________________________________________________

2. I Still Cook Me in the kitchen, Yokohama (my birthplace), 1953 _________________________________________________________

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4. Is this not cool? I took Hotelling’s position at UNC, then Fan took mine My photo was taken at the Wichita Mountains, December 1999 (by me)

5. College Station, home of Texas A&M University I-35 I-45 Big Bend National Park Wichita Falls, Wichita Falls, that’s my hometown West Texas Palo Duro Canyon, the Grand Canyon of Texas Guadalupe Mountains National Park East Texas 

6. Palo Duro Canyon of the Red River

7. What I am Not I know that potato chips are not a basic healthy food group. However, if you ask me a detailed question about nutrition, then I will ask Joanne LuptonNancy TurnerMeeyoung Hong _________________________________________________________

8. You are what you eat, but do you know who you are? This talk is concerned with a simple question. Will lowering her intake of fat decrease a woman’s chance of developing breast cancer? _________________________________________________________

9. Basic Outline Diet affects health. Many (not all!) studies though are not statistically significant. Focus: quality of the instruments used to measure diet Conclusion #1: The usual instruments are largely to blame. Conclusion #2: Expect studies to disagree _________________________________________________________

10. Evidence in Favor of the Fat- Breast Cancer Hypothesis Animal studies Ecological comparisons Case-control studies _________________________________________________________

11. International Comparisons _____________________________________________________________

12. Evidence against the Fat-Breast Cancer Hypothesis Prospective studies These studies try to assess a woman’s diet, then follow her health progress to see if she develops breast cancer The diets of those who developed breast cancer are compared to those who do not Prior to 2007, only 1 prospective study has found evidence suggesting a fat and breast cancer link, and 1 has a negative link _________________________________________________________

13. Prospective Studies NHANES (National Health and Nutrition Examination Survey): n = 3,145 women aged 25-50 Nurses Health Study: n = 60,000+ Pooled Project: n = 300,000+ Norfolk (UK) study: n = 15,000+ _________________________________________________________

14. The Nurses Health Study, Fat and Breast Cancer _________________________________________________________ 60,000 women, followed for 10 years Prospective study Note that the breast cancer cases were announcing that they eat less fat Donna Spiegelman, the NHS statistician

15. Clinical Trials The lack of consistent (even positive) findings led to the Women’s Health Initiative Approximately 40,000 women randomized to two groups: healthy eating and typical eating _________________________________________________________

16. WHI Diet Study Objectives _________________________________________________________

17. Prior Objections to WHI Cost ($415,000,000) Whether North Americans can really lower % Calories from Fat to 20%, from the current 38% Even if the study was successful, difficulties in measuring diet mean that we will not know what components led to the decrease in risk. _________________________________________________________

18. Change in Fat Calories Over Time _________________________________________________________ Result from WHI Diet Clinical Trial Women reported a decrease in fat- calories, but not to 20%

19. How do we measure diet in humans? 24 hour recalls Diaries Food Frequency Questionnaires (FFQ) _________________________________________________________ Walt Willett has a popular book and a popular FFQ

20. Food diaries Hot topic at NCI Only measures a few day’s diet, not typical diet A single 3-day diary finding a diet-cancer link is not universally scientifically acceptable Need for repeated applications Induces behavioral change?? _________________________________________________________

21. Typical (Median) Values of Reported Caloric Intake Over 6 Diary Days: WISH Study

22. The Food Frequency Questionnaire _________________________________________________________

23. The Pizza Question _________________________________________________________

24. The Norfolk Study with ~Diaries and FFQ _________________________________________________________ 15,000 women, aged 45-74, followed for 8 years 163 breast cancer cases Diary: p = 0.005 FFQ: p = 0.229

25. Summary FFQ does not find a fat and breast cancer link 24 hour recalls and diaries are expensive They have found links, but in opposite directions Diaries may modify behavior Question: do any of these things actually measure dietary intake? How well or how badly? These are statistical questions! _________________________________________________________

26. Do We Know Who We Are? Karl Pearson was arguably the 1 st great modern statistician Pearson chi-squared test Pearson correlation coefficient _________________________________________________________ Karl Pearson at age 30

27. Do We Know Who We Are? Pearson was deeply interested in self- reporting errors In 1896, Pearson ran the following experiment. For each of 3 people, he set up 500 lines of a set of paper, and had them bisected by hand _________________________________________________________ A gaggle of lines

28. Pearson’s Experiment He then had an postdoc measure the error made by each person on each line, and averaged “Dr. Lee spent several months in the summer of 1896 in the reduction of the observations ” _________________________________________________________ A gaggle of lines, with my bisections

29. Pearson’s Personal Equations Pearson computed the mean error committed by each individual: the “personal equations “ He found: the errors were individual. His errors were to the right, Dr. Lee’s to the left _________________________________________________________ Karl Pearson in later life

30. What Do Personal Equations Mean? Given the same set of data, when we are asked to report something, we all make errors, and our errors are personal In the context of reporting diet, we call this “person-specific bias “ _________________________________________________________ Laurence Freedman of NCI, with whom I did the work

31. Model Details for Statisticians The model in symbols The existence of person-specific bias means that variance of true intake is less than one would have thought _________________________________________________________

32. Model Details for Statisticians We fit a linear mixed model The OPEN Study had the following measurements Two FFQ Two Protein biomarkers Two Energy biomarkers _________________________________________________________

33. Our Hypothesis We hypothesized that when measuring Fat intake The personal equation, or person-specific bias, unique to each individual, is large and debilitating. The problem: the actual variability in American diets is much smaller than suspected. _________________________________________________________

34. Can We Test Our Hypothesis? We need biomarker data that are not much subject to the personal equation There is no biomarker for Fat  There are biomarkers for energy (calories) and Protein We expect that studies are too small by orders of magnitude _________________________________________________________

35. Biomarker Data Calories and Protein: Available from NCI’s OPEN study Results are surprising Victor Kipnis was the driving force behind OPEN _________________________________________________________

36. Sample Size Inflation There are formulae for how large a study needs to be to detect a true doubling of risk from low and high Fat/Energy Diets These formulae try to account for measurement error These formulae ignore the personal equation We recalculated the formulae _________________________________________________________

37. Biomarker Data: Sample Size Inflation _________________________________________________________ If you are interested in the effect of calories on health, multiply the sample size you thought you needed by 11. For protein, by 4.5

38. Relative Risk _________________________________________________________ If high calories increases the risk of breast cancer by 100% in fact, and you change your intake dramatically, the FFQ thinks doing so increases the risk by 4% Result: It is not possible to tell if changing your absolute caloric intake, or your fat intake, or your protein intake will have any health effects

39. Relative Risk, Food Composition _________________________________________________________ If high protein (fat) increases the risk of breast cancer by 100%, your calories remain the same, you dramatically lower your protein (fat) intake, then FFQ thinks your risk increases by 20%- 30% Result: It is pretty difficult to tell if changing your food composition while maintaining your caloric intake will have any health effects

40. New Results The AARP Study: 250,000+ women, by far the greatest number in any single study Results: Huge size  statistical significance FFQ  small measured increase in risk for dramatic behavioral change (1.32 after correction) Statistician’s dream: use Pearson’s idea to get at the true increase in risk _________________________________________________________ A happy statistician dreaming about AARP

41. New Results The WHI Controls Study: 30,000+ women All with > 32% Calories from Fat via FFQ Diaries in a nested case- control study Highly significant fat effect in the diaries (Observed RR in quantiles = 1.6) _________________________________________________________ A happy statistician doing field biology in the Kimberley

42. Summary WHI, 2006, clinical trial My best case conjecture in 2005: Probably no statistically significant effects The p-value was 0.07, relative risk about 1.2 My best case conjecture in 2008 after further follow-up Statistically significant, modest effects _________________________________________________________

43. You are what you eat, but do you know who you are? Diet is incredibly hard to measure Even 100% increases in risk cannot be seen in large cohort studies with an FFQ If you read about a diet intervention, measured by a FFQ, and it achieves statistical significance multiple times: wow! _________________________________________________________

44. You are what you eat, but do you know who you are? Much work at NCI and WHI and EPIC on new ways of measuring diet EPIC (a multi-country study) may be a model, because of the wide distribution of intakes _________________________________________________________

45. What Was Done The OPEN analysis actually fit Protein and Energy together. We call this the Seemingly Unrelated Measurement Error Model Can get major gains in efficiency _________________________________________________________

46. SUMEM Gains in efficiency come from the correlations of the random effects _________________________________________________________

47. Model Details for Statisticians The model in symbols Linear mixed model, fit by PROC MIXED _________________________________________________________

48. Attenuation The attenuation is the slope in the linear regression of X on Q _________________________________________________________

49. Relative Risk and Attenuation Start with a logistic model True relative risk Observed relative risk (regression calibration) _________________________________________________________

50. Relative Risk and Attenuation _________________________________________________________ AttenuationRelative Risk 1.0 (no meas. Error)2.0 0.81.74 0.51.41 0.251.19 0.101.07