Measuring Dietary Intake Raymond J. Carroll Department of Statistics Faculty of Nutrition and...
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Transcript of Measuring Dietary Intake Raymond J. Carroll Department of Statistics Faculty of Nutrition and...
Measuring Dietary Intake
Raymond J. CarrollDepartment of Statistics
Faculty of Nutrition and Faculty of Toxicology
Texas A&M Universityhttp://stat.tamu.edu/~carroll
_________________________________________________________
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 DuroCanyon, 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 Lupton Nancy Turner Meeyoung 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 woman’s 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 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
_________________________________________________________
Prospective Studies
• NHANES (National Health and Nutrition Examination Survey): n = 3,145 women aged 25-50
• 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 Women’s 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_________________________________________________________
0
5
10
15
20
25
30
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40
Y-0 Y-1 Y-3 Y-6
Control
Intervention
Goal
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 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??
_________________________________________________________
1350140014501500155016001650170017501800
FF
Q
Dia
ry 1
Dia
ry 2
Dia
ry 3
Dia
ry 4
Dia
ry 5
Dia
ry 6
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 = 0.005
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 1st 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
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
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
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
_________________________________________________________
iij 0 1
2r
2ε
i
i
ij
i
i
j
Q =β + β + + ;
=true intake;
=personal equation=Normal(0,σ );
=random error =Normal(0,
r
X
ε
ε σ
rX
)
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
_________________________________________________________
iij 0Q 1Q
i
iQ
i Fj i
ijQ
j
Q =β +β + +ε
UX
;
M = +
rX
;
Attenuation
• The attenuation is the slope in the linear regression of X on Q
_________________________________________________________
ijQ
ijF
iQij 0Q 1Q
ij
Q
i
i
Q =β +β + + ;
M = + ;
λ =cov( ,Q)/ v
ε
ε
a
X
X
X
r
r(Q)
Relative Risk and Attenuation
• Start with a logistic model
• True relative risk
• Observed relative risk (regression calibration)
0 1pr(D=1)=H X( + )
_________________________________________________________
1R exp( )
QλQR R since λ < 1
Relative Risk and Attenuation_________________________________________________________
Attenuation Relative Risk
1.0 (no meas. Error) 2.0
0.8 1.74
0.5 1.41
0.25 1.19
0.10 1.07
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 NCI’s
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
0
2
4
6
8
10
12P
rote
in
Ca
lorie
s
%-
Prote
in
_________________________________________________________
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%
1
1.2
1.4
1.6
1.8
2
Relative Risk ForChanging Your Food
Intake
True: 2.00
ObservedProtein: 1.09
ObservedCalories: 1.04
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%
1
1.2
1.4
1.6
1.8
2
Relative Risk for FoodComposition
True: 2.00
ObservedProteinDensity: 1.31
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
Statistician’s dream: use Pearson’s 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
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ijP 0QP 1QP
ij
iP
iP
iE
i
ijQP
ijQP
iQP
i
P
ijE QE0QE 1QE
ijE
ijQE
ijQE E
Q =β +β + + ;
M = + ;
Q =β + β + + ;
M = + ;
X
X
X
U
X
ε
U
r
εr