Title: The Women
1The 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.
2Major 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?
3Some 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
4Design of WHI
os
5WHI 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
6Clinical 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.
8Cox Model h(t Z(t)) hos(t)exp(x(t)/ß)
9CVD 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.
10EP Hazard Ratio in the CT and OS as a Function
of Time from Initiation of EP Use Coronary
Heart Disease
11Difference in Distribution in Years from EP
Initiation between WHI Cohorts
12Ratio 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)
14Postmenopausal 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
15Hormone 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
16Coronary 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
17Invasive 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.
18Invasive 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
19Breast Cancer Hazard Ratio Estimates according to
Prior Postmenopausal Hormone Therapy Status and
Years from Hormone Therapy Initiation
20Distribution 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.
21Breast 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
22Estimated 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
23Estimated 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)
24Factors 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
25Factors 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.
26Lessons 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.
27Enhanced 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
28Age-Adjusted Breast Cancer Incidence among Women
of Ages 55-69 in 1980 versus per capita for
Consumption in 1975
29Dietary 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?
30Underreporting of Energy and Protein (Heitmann
and Lissner, 1995, BMJ)
31Dietary 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
32Mean (SD) of Nutrient Consumption by
Randomization Group
Difference significant at plt0.001 from a two
sample t-test
33Comparison 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.
35Nutrient 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.
36Associations of Participants Characteristics with
Measurement Error in Self-Reported Diet in the
Womens Health Initiative Nutritional Biomarkers
Study
37Measurement 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
38Regression Calibration Coefficients for
Log-Transformed Total Energy, Total Protein and
Percent Energy from Protein
39Intermediate 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
40Hormone 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
41IPAS
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
42Data acquisition from 5 million mass spectra
43Protein quant_common
EP
E
952
1,054
698
44(No Transcript)
45Candidates for validation assay
-Angiogenin, RNASE4
-Insulin-like growth factor, IGF1
-Insulin-like growth factor binding protein1,
IGFBP1
-Zinc-alpha-2-glycoprotein, AZGP1
Other candidates?
46Population 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?
47Population 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