The Women

1
The Womens Health Initiative, Cohort Studies,
and the Population Science Research Agenda

• Ross L. Prentice
• Fred Hutchinson Cancer Research Center
• and University of Washington

• How can we obtain answers concerning health
  benefits and risks of behavior changes
  (interventions), and know that the answers are
  reliable?
• Major research tools each have important
  limitations (RCT intermediate outcome trial
  cohort and case-control studies)
• Most population science research is
  outcome-centric, rather than intervention-centric.
  
• Suitable forums for identifying priority research
  opportunities and needed methodology development
  are generally lacking.

2
Major Research Tools Each Have Important
Limitations

• Randomized controlled intervention trials
• Cost, logistics, intervention adherence?
• Only a small number are feasible at any time.
• Intermediate outcome clinical trials
• Sufficiently comprehensive outcomes?
• Methods to integrate data across many short-term
  outcomes?
• Ability to replace full-scale clinical outcome
  trial? (surrogate outcomes)
• Observational studies
• When are potential biases negligible?
  (confounding, selection, measurement error)
• What assurance can be provided by replication in
  multiple populations?
• How does reliability depend on nature of exposure
  variable/potential intervention and its
  measurement characteristics?

3
Some Possible Ways Forward

• Comparative and joint analysis of RCT and
  Observational Study data
• Differences may reflect residual bias in
  observational study (or differences in study
  populations limitations of data analysis
  procedures study power or adherence issues, or
  differential outcome ascertainment in either
  study type).
• Joint analyses may usefully extend RCT results.
• Enhanced role of biomarkers to strengthen each
  type of study
• Biomarkers to calibrate difficult-to-measure
  exposures in observational studies, and for
  explanatory analysis of intervention effects on
  RCTs.
• Biomarkers to enhance comprehensiveness of
  intermediate outcome RCTs.
• Cooperative group to advise NIH and other funding
  sources on research
• opportunities and needs in chronic disease
  population research

4
Design of WHI
os
5
WHI Hormone Program Design
Conjugated equine estrogen (CEE) 0.625 mg/d
YES
N 10,739
Placebo
Hysterectomy
CEE 0.625 mg/d medroxyprogesterone acetate
(MDA) 2.5 mg/d
NO
N 16,608
Placebo
6
Clinical Outcomes in the WHI Postmenopausal
Hormone Therapy Trials (JAMA 2002, 2004)
7
Postmenopausal Hormone Therapy (EP) and
Cardiovascular Disease

• Womens Health Initiative study of estrogen plus
  progestin among postmenopausal women in the age
  range 50-79 at baseline
• 
  CT
  OS
  
  Age-adj
  Age-adj
• 
  Placebo EP HR
  Control EP HR
• Number of women 8102 8506 35,551 17,503
• Number of events
• CHD 147 188 1.21
  615 158 0.71
• Stroke 107 151 1.33 490 123 0.77
  
• VT 76 167 2.10 336 153 1.06

Prentice RL, Langer R, Stefanick ML, Howard BV,
Pettinger M, Anderson G, Barad D, Curb D, Kotchen
J, Kuller L, Limacher M, Wactawski-Wende J.
American Journal of Epidemiology 162404-414
2005.
8
Cox Model h(t Z(t)) hos(t)exp(x(t)/ß)
9
CVD Hazard Ratios for EP Use, in Joint Analyses
of Data from CT and OS Cohorts, Controlling for
Potential Confounding Factors
Adjusted for age (linear), ethnicity, bmi
(categorical plus linear), education, smoking,
age at menopause, physical functioning.
10
EP Hazard Ratio in the CT and OS as a Function
of Time from Initiation of EP Use Coronary
Heart Disease
11
Difference in Distribution in Years from EP
Initiation between WHI Cohorts
12
Ratio of OS to CT Hazard Ratios for EP Use
13

• EP Hazard Ratios (95 CIs) as a Function of
  Years from EP Initiation, and Average HRs over
  Various Times from EP Initiation, Assuming
  Common HR Functions in the CT and OS
• Years from Coronary Heart
  Disease Venous Thromboembolism
• EP Initiation HR (95
  CI) HR (95 CI)
  
• lt 2 1.56 (1.12, 2.19) 2.87
  (1.89, 4.35)
• 2 5 1.16 (0.89, 1.51) 1.70
  (1.28, 2.26)
• gt 5 0.81 (0.67, 0.99) 1.26
  (1.02, 1.56)
• Average
  HR (95 CI) Average HR (95 CI)
• 2 1.56 (1.12, 2.19) 2.87
  (1.89, 4.35)
• 4 1.36 (1.09, 1.70) 2.28
  (1.72, 3.03)
• 6 1.27 (1.04, 1.54) 2.07
  (1.62, 2.63)
• 8 1.13 (0.96, 1.33) 1.83
  (1.50, 2.23)
• 10 1.07 (0.92, 1.24) 1.71 (1.43,
  2.05)

14
Postmenopausal Estrogen-alone and Cardiovascular
Disease (Prentice RL, Langer R, Stefanick ML, et
al. AJE 163589-599,2006)

CT
OS
Age-adj
Age-Adj
Placebo E-alone
HR Control E-alone HR
Number of women 5,429 5,310
16,411 21,920 Number of
events CHD 201 217
0.96 548 421
0.68 Stroke 127 168
1.37 408 431
0.95 VT 86 111
1.33 274 265 0.78


15
Hormone Treatment Hazard Ratios (95 CIs) in the
Estrogen (E-alone) Clinical Trial (CT) and in
the Estrogen and Estrogen plus Progestin (EP)
Clinical Trials and Corresponding Observational
Study Samples
16
Coronary Heart Disease Hormone Treatment Hazard
Ratios (95 CIs) among Women 50-59 Years of Age
at Baseline from the OS with Adjustment using CT
and OS Data on the Alternative Preparation
17
Invasive Breast Cancer Incidence Rates in the
Clinical Trial Hormone Trials (HT) and the
Observational Study (OS) Subcohort
E-alone
EP

From Prentice RL, Chlebowski R, Stefanick M,
Manson J, Langer R, Pettinger M, Hendrix S,
Hubbell A, Kooperberg C, Kuller L, Lane D,
McTiernan A, OSullivan MJ, Anderson G (2007).
To appear, AJE (E-alone). Revised for AJE
(EP). Age-adjusted to the 5-year age
distribution in the CT cohort.
18
Invasive Breast Cancer Hazard Ratios for HT Use
Adjusted for Potential Confounding Factors, in
Combined Analyses of Data from the CT and OS
Adjusted for age (linear), ethnicity, bmi
(categorical and linear), education, smoking
history, alcohol consumption, prior HT use,
general health, physical activity, Gail risk score
19
Breast Cancer Hazard Ratio Estimates according to
Prior Postmenopausal Hormone Therapy Status and
Years from Hormone Therapy Initiation
20
Distribution of Women in the WHI Hormone Therapy
Clinical Trials (CT), and in Corresponding
Observational Study (OS) Subcohorts, According to
Prior Use of Postmenopausal Hormone Therapy (HT)
and Gap Time from Menopause to First Use of HT,
Among Hormone Therapy Users
Prior HT is defined relative to WHI enrollment
in the CT and in the non-user groups in the OS.
Prior HT in the user groups in the OS is defined
relative to the beginning of the on-going HT
episode at enrollment.
21
Breast Cancer Hazard Ratio Estimates according to
Prior Postmenopausal Hormone Therapy Status,
Years from Hormone Therapy Initiation, and Gap
Time from Menopause to Hormone Therapy
Initiation, among Women Adhering to their
Baseline Hormone Therapy Status
Gap time in years from menopause to first use of
HT
22
Estimated Hazard Ratios (HRs) for CEE and CEE/MPA
for Women Who Begin Hormone Therapy (HT)
Immediately Following the Menopause and Adhere to
their HT Regimen, from Combined Analysis of WHI
Clinical Trial (CT) and Observational Study (OS)
Data
23
Estimated Hazard Ratios (HRs) for CEE and CEE/MPA
for Women Who Begin Hormone Therapy (HT)
Immediately Following the Menopause and Adhere to
their HT Regimen, from Combined Analysis of WHI
Clinical Trial (CT) and Observational Study (OS)
Data (continued)
24
Factors Included in Observational Study (OS)
Hazard Ratio Analyses to Control Confounding.
Corresponding Coefficients are Estimated
Separately for Subsets of Women With or Without
Prior Postmenopausal Hormone Therapy (HT)
BMI, body mass index
25
Factors Included in Observational Study (OS)
Hazard Ratio Analyses to Control Confounding.
Corresponding Coefficients are Estimated
Separately for Subsets of Women With or Without
Prior Postmenopausal Hormone Therapy (HT)
(continued)
NSAID, non-steroidal anti-inflammatory drug OC,
oral contraceptive These factors included only
for women with prior hormone therapy.
26
Lessons from Comparative and Joint CT and OS
Analysis of Postmenopausal Hormone Therapy Effects

• Ability to control prescription/confounding
  biases in OS may differ by clinical outcome
  (e.g., stroke, hip fracture).
• Careful design and analysis methods needed to
  obtain accurate information from observational
  studies (allow for departures from proportional
  hazards, possible effect modification, ).
• Clinical trial and observational study data may
  be able to be combined to obtain useful benefits
  and risk assessments (important subsets, longer
  durations, ).
• Intervention trials may be needed if public
  health implications are sufficiently great.
• Comparative trial and observational study results
  for other preventive interventions could be
  informative.

27
Enhanced Role for Biomarkers in Population
Science Research

• Exposure biomarkers for difficult to measure
  exposures (e.g., dietary consumption or physical
  activity patterns)
• High-dimensional biologic data to augment value
  of intermediate outcome trials
• e.g., Dietary fat and cancer

28
Age-Adjusted Breast Cancer Incidence among Women
of Ages 55-69 in 1980 versus per capita for
Consumption in 1975
29
Dietary Fat and Postmenopausal Breast Cancer Fat
Intake Quintile

Case-control Studies Howe et al (1990, JCNI) 1
1.20 1.24 1.24 1.46
(plt0.0001) Cohort Studies Hunter et al (1996,
NEJM) 1 1.01 1.12 1.07 1.05
(p 0.21) Any reason to continue research on
this topic? Ability to adequately characterize
and adjust for measurement error?
30
Underreporting of Energy and Protein (Heitmann
and Lissner, 1995, BMJ)
31
Dietary Change GoalsIntervention Group

• 20 energy from fat
• 5 or more fruit and vegetable servings daily
• 6 or more grain servings daily

Photos courtesy of USDA Agricultural Research
Service
32
Mean (SD) of Nutrient Consumption by
Randomization Group
Difference significant at plt0.001 from a two
sample t-test
33
Comparison of Cancer Incidence Rates between
Intervention and Comparison Groups in the Womens
Health Initiative (WHI) Dietary Modification
Trial
Trial includes 19,541 women in the intervention
group and 29,294 women in the comparison
group. Weighted log-rank test (two-sided)
stratified by age (5-year categories) and
randomization status in the WHI hormone therapy
trial. Weights increase linearly from zero at
random assignment to a maximum of 1.0 at 10
years. HR hazard ratio CI confidence
interval, from a proportional hazards model
stratified by age (5-year categories), and
randomization status in the WHI hormone therapy
trial.
34

• Nature and magnitude of random and systematic
  bias likely
• varies among assessment instruments.
• Systematic bias may relate to many factors (e.g.,
  age,
• ethnicity, body mass, behavioral factors).
• Bingham et al (2003, Lancet) report a positive
  association between breast cancer and total and
  fat when consumption was assessed using a 7-day
  food diary, but the association was modest and
  non-significant when consumption was assessed
  with a FFQ. Very similar results from 4-day food
  record and FFQ analyses among DM comparison group
  women (Freedman et al 2006, IJE).
• Objective measures (biomarkers) are needed to
  make progress in this important research area.
  Biomarker assessments in substudies (such as DLW
  measures of total energy expenditure) can be used
  to calibrate self-report assessments.

35
Nutrient Biomarker Substudy in the WHI DM Trial
and Nutrition and Physical Activity Assessment
Study in WHI Observational Study

• 544 women completed two-week DLW protocol with
  urine and blood collection and with FFQ and other
  questionnaire data collection (50 intervention,
  50 control). A 20 reliability subsample
  repeated protocol separated, by about 6 months
  from original data collection.
• Biomarker study among 450 women in the WHI
  Observational Study for calibrating baseline FFQ,
  4DFR, and PA questions, and for evaluating
  measurement properties of prominent dietary and
  physical activity assessment approaches
  (frequencies, records, and recalls) and their
  combination.

36
Associations of Participants Characteristics with
Measurement Error in Self-Reported Diet in the
Womens Health Initiative Nutritional Biomarkers
Study
37
Measurement Models for Nutritional
Epidemiology(Carroll, Freedman, Kaaks, Kipnis,
Spiegelman, Rosner, Prentice)

• Recovery Biomarkers
• Xbiomarker Z e
• Wself-report a0 a1Z a2V a3ZV r e
• Can estimate odds ratios (Sugar et al,
  2007,Bmcs), or hazard ratios (Shaw et al,
  2007), corresponding to Z from cohort data on W
  and subcohort data on X.
• Concentration Biomarkers
• Xbiomarker b0 b1Z s e
• Inability to disassociate actual intake Z from
  person-specific bias s is a major limitation.
• Needed research
• Development of additional recovery-type
  biomarkers
• Methodologic work (e.g., feeding study designs)
  to facilitate use of concentration biomarkers

38
Regression Calibration Coefficients for
Log-Transformed Total Energy, Total Protein and
Percent Energy from Protein
39
Intermediate Outcome Trials Having
High-Dimensional Responses

• Evaluate impact of candidate preventive
  interventions on high-dimensional response (e.g.,
  plasma proteome)
• Develop knowledge base to relate high-dimensional
  response to risk of a broad range of clinical
  outcomes
• Predict intervention effects on clinical outcomes
  of interest, from high-dimensional response, to
  help determine whether a full-scale intervention
  trial is merited

40
Hormone Therapy Proteomics Project

• Intact Protein Analysis System of Dr. Samir
  Hanash
• 50 E-alone women 50 EP women
• Compare baseline to 1-year serum proteome
• in pools of size 10

41
IPAS
5 EP 132 x 5660
5 E 132 x 5660
Total 1,320 fractions
Faca V et al., J. Proteome Res., 5, 2006,
2009-2018
Quantitative analysis of acrylamide labeled
serum proteins by LC-MS/MS
Faca V et al., J. Proteome Res., 2007 accepted
Contribution of protein fractionation to depth
of analysis of the serum and plasma proteomes
42
Data acquisition from 5 million mass spectra
43
Protein quant_common
EP
E
952
1,054
698
44
(No Transcript)
45
Candidates for validation assay
-Angiogenin, RNASE4
-Insulin-like growth factor, IGF1
-Insulin-like growth factor binding protein1,
IGFBP1
-Zinc-alpha-2-glycoprotein, AZGP1
Other candidates?
46
Population Science Research Needs

• An enhanced preventive intervention development
  enterprise
• Observational studies of maximal reliability for
  promising intervention concepts
• Full-scale intervention trials when rationale
  strong enough, and public health potential
  sufficiently great
• Vigorous methodology development (e.g., to
  incorporate exposure and intermediate outcome
  biomarkers into research agenda)
• Infrastructure to facilitate?

47
Population Science Cooperative Group

• Identify preventive interventions that merit
  initial testing or full-scale evaluation
• Receive and evaluate preventive trial proposals
• Identify and facilitate needed methodologic
  research
• Group Composition
• Population, basic and clinical scientists
• Leaders in key areas for intervention development
• Leaders in major chronic disease research areas
• Representatives from within and outside of NIH