Measuring Dietary Intake

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

2. Where I am From _________________________________________________________ Wichita Falls (Ranked #10 in the worst jobs in Texas by Texas Monthly, 1980) Best pecans in the world

3. 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 Lupton Nancy Turner Meeyoung Hong

4. 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? • This is a hugely controversial question • The debate has a huge statistical component • It is also relevant to questions such as: if I lower my caloric intake, will I live longer?

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

6. International Comparisons _____________________________________________________________ • There are major differences in fat and saturated fat intake across countries • Are these related to breast cancer?

7. International Comparisons _____________________________________________________________

8. Case-control studies _________________________________________________________ • Find women who have breast cancer, and women who do not. • Compare their current fat intakes • A problem on its face. We want past intake, not after-the-fact intake • Not much found in single studies, but pooling them over many diverse studies suggests a fat-breast cancer link

9. 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 • Only (?) 1 prospective study has found firm evidence suggesting a fat and breast cancer link, and 1 has a negative link

10. 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+ • AARP: n = 250,000+ • WHI Controls n = 30,000+ • AARP and WHI available soon

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

12. Clinical Trials _________________________________________________________ • The lack of consistent (even positive) findings led to the Women’s Health Initiative • Approximately 60,000 women randomized to two groups: healthy eating and typical eating

13. WHI Diet Study Objectives _________________________________________________________

14. Objections to WHI _________________________________________________________ • Cost ($100,000,000+) • Whether Americans can really lower % Calories from Fat to 20%, from the current 35% • Even if the study is successful, difficulties in measuring diet mean that we will not know what components led to the decrease in risk. Ross Prentice of the WHI

15. 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

16. Objections to the 24 hour recall _________________________________________________________ • Only measures yesterday’s diet, not typical diet • A single 24 hour recall finding a diet-cancer link is not universally scientifically acceptable • Need for repeated applications • Expensive • Personal interview • Phone interview

17. NHANES: Fat is Protective (?) _________________________________________________________ Typical % Calories from Fat Cases: 35% Controls: 37%

18. NHANES: Calories are Protective (?) _________________________________________________________ Typical Calories Cases: 1,300 Controls: 1,500

19. 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??

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

21. The Food Frequency Questionnaire _________________________________________________________ • Do you remember the SAT?

22. The Pizza Question _________________________________________________________

23. 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 Directly contradicts NHANES (women aged 25-50).

24. 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 also appear to modify behavior • Question: do any of these things actually measure dietary intake? • How well or how badly? • These are statistical questions! 

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

26. 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

27. 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

28. 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

29. What Do Personal Equations Mean? _________________________________________________________ Laurence Freedman of NCI, with whom I did the work • 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 “

30. What errors do FFQ Make? _________________________________________________________ • Pretend you and I eat the same amount of fat on average. • We each fill out a FFQ twice, take the mean fat intake from the FFQ, and get different answers. Why? • Random Error: I will give different answers each time • No one reports all the ice cream he/she eats (fixed bias due to societal factors) • Personal Equation: we all report differently

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

32. 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. • If true, the hypothesis says that one cannot really do an epidemiologic study for total energy or total fat, with any degree of success for cancer

33. 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

34. Biomarker Data _________________________________________________________ Victor Kipnis was the driving force behind OPEN • Protein: • Available from a number of European studies • Calories and Protein: • Available from NCI’s OPEN study • Results are surprising

35. Sample Size Inflation _________________________________________________________ • There are formulae for how large a study needs to be to detect a doubling of risk from low and high Fat/Energy Diets • These formulae ignore the personal equation • We recalculated the formulae accounting for • Random error in repeated FFQ • Societal factors causing underreporting in general • Pearson’s personal equation: we report differently

36. 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

37. Relative Odds _________________________________________________________ • Suppose high fat/energy/? diets lead to twice the risk of breast cancer compared to low fat/energy • This is called the Relative Risk • What is the risk we would observe with the FFQ?

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 very difficult to tell if changing your food composition while maintaining your caloric intake will have any health effects

40. Summary _________________________________________________________ • Trying to establish a Fat and Breast Cancer link has proved difficult • Standard instruments hide effects • 24 hour recalls have found effects, but are very expensive • Diaries may(?) change behavior: difficult to believe what they say • There is hope to analyze food composition, not absolute intakes

41. Summary _________________________________________________________ • The AARP Study: 250,000+ women, by far the greatest number in any study • My best case conjecture: • Huge size  statistical significance • FFQ  small measured increase in risk for dramatic behavioral change • Statistician’s dream: use Pearson’s idea to get at the true increase in risk A happy statistician dreaming about AARP

42. Summary _________________________________________________________ • The WHI Controls Study: 30,000+ women • All with > 32% Calories from Fat via FFQ • Also includes diaries • Will be able to compare diaries and FFQ • How many studies with 30,000+ diaries can we afford? A happy statistician doing field biology in Northwest Australia (the Kimberley)

43. Summary _________________________________________________________ • WHI, 2005, clinical trial • My best case conjecture: • Probably no statistical effects (?) • Even if so, the FFQ is so bad that we will not know what to do: • Decrease Fat? • Decrease saturated Fat? • Eat more grain? • Eat more veggies (yuck)?

44. 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 studies • If you read about a diet intervention, measured by a FFQ, and it achieves statistical significance multiple times: wow!

45. 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 may be a model, because of the wide distribution of intakes

46. Reporting Biases _________________________________________________________ • FFQ are not very good for measuring caloric intake • We do not want to admit our pizza, ice cream, etc.

47. Reporting Biases _________________________________________________________ • 24 hour recalls are not very good for measuring caloric intake • They are better than FFQ (less bias, for example), but they still are not very good

48. Reporting Biases _________________________________________________________ • FFQ are better for % Calories from Protein • Our food composition is better known to us than the amounts • Inflation of sample size only 2.3, not 4.5 as for actual protein