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

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

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

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 _________________________________________________________

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 womans chance of developing breast cancer? _________________________________________________________

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 instruments are largely to blame. Conclusion #2: Expect studies to disagree _________________________________________________________

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

International Comparisons _____________________________________________________________

Evidence against the Fat-Breast Cancer Hypothesis Prospective studies These studies try to assess a womans 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 _________________________________________________________

Prospective Studies NHANES (National Health and Nutrition Examination Survey): n = 3,145 women aged Nurses Health Study: n = 100,000+ Pooled Project: n = 300,000+ Norfolk (UK) study: n = 15,000+ _________________________________________________________

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

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

WHI Diet Study Objectives _________________________________________________________

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. _________________________________________________________

Change in Fat Calories Over Time _________________________________________________________ Women reported a decrease in fat- calories, but not to 20%

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

Food diaries Hot topic at NCI Only measures a few days 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?? _________________________________________________________

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

The Food Frequency Questionnaire Do you remember the SAT? _________________________________________________________

The Pizza Question _________________________________________________________

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

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! _________________________________________________________

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

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

Pearsons 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

Pearsons Personal Equations Pearson computed the mean error committed by each individual: thepersonal equations He found: the errors were individual. His errors were to the right, Dr. Lees to the left _________________________________________________________ Karl Pearson in later life

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

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 _________________________________________________________

Model Details for Statisticians The OPEN Study had the following measurements Two FFQ Two Protein biomarkers Two Energy biomarkers _________________________________________________________

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

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

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

Relative Risk and Attenuation _________________________________________________________ AttenuationRelative Risk 1.0 (no meas. Error)

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. _________________________________________________________

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 _________________________________________________________

Biomarker Data Calories and Protein: Available from NCIs OPEN study Results are surprising Victor Kipnis was the driving force behind OPEN _________________________________________________________

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 _________________________________________________________

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

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

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

New Results The AARP Study: 250,000+ women, by far the greatest number in any single study Results according to rumor: Huge size statistical significance FFQ small measured increase in risk for dramatic behavioral change Statisticians dream: use Pearsons idea to get at the true increase in risk _________________________________________________________ A happy statistician dreaming about AARP

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 (RR in quantiles of 1.6) _________________________________________________________ A happy statistician doing field biology in Northwest Australia (the Kimberley)

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 _________________________________________________________

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! _________________________________________________________

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 _________________________________________________________

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 _________________________________________________________

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