1. The Women’s 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?Cost, logistics, intervention adherence? Only a small number are feasible at any time.Only a small number are feasible at any time. Intermediate outcome clinical trials Sufficiently comprehensive outcomes?Sufficiently comprehensive outcomes? Methods to integrate data across many short-term outcomes?Methods to integrate data across many short-term outcomes? Ability to replace full-scale clinical outcome trial? (surrogate outcomes)Ability to replace full-scale clinical outcome trial? (surrogate outcomes) Observational studies When are potential biases negligible? (confounding, selection, measurement error)When are potential biases negligible? (confounding, selection, measurement error) What assurance can be provided by replication in multiple populations?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?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).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.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 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.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 Hysterectomy Conjugated equine estrogen (CEE) 0.625 mg/d Placebo CEE 0.625 mg/d + medroxyprogesterone acetate (MDA) 2.5 mg/d N= 16,608 N= 10,739 YES NO Placebo

6. Clinical Outcomes in the WHI Postmenopausal Hormone Therapy Trials (JAMA 2002, 2004)

7. Postmenopausal Hormone Therapy (E+P) and Cardiovascular Disease Women’s 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 E+P HR Control E+P HR Number of women8102 850635,551 17,503 Number of events: CHD 147 188 1.21 615 158 0.71 Stroke 107 1511.33 490 123 0.77 VT 76 1672.10 336 153 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 162:404-414; 2005.

8. Cox Model h(t; Z(t)) = h os (t)exp(x(t) / β)

9. HR (95% CI) FactorCHDStrokeVT E+P in CT1.27 (1.00, 1.61)1.21 (0.93, 1.59)2.13 (1.59, 2.85) E+P in OS0.87 (0.72, 1.05)0.86 (0.70, 1.07)1.31 (1.07, 1.61) E+P in OS/E+P in CT0.70 (0.52, 0.95)0.72 (0.52, 1.02)0.62 (0.43, 0.88) CVD Hazard Ratios for E+P 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. Years From E+P Initiation HR in CTHR in OS HR in CT Under Constant OS to CT Ratio HR Common to CT and OS  2 2 1.68 (1.15, 2.45; 80)1.12 (0.46, 2.74; 5)1.58 (1.12, 2.24)1.56 (1.12, 2.19) (2, 5)1.25 (0.87, 1.79; 80)1.05 (0.70, 1.58; 27)1.19 (0.87, 1.63)1.16 (0.89, 1.51) > 50.66 (0.36, 1.21; 28)0.83 (0.67, 1.01; 126)0.86 (0.59, 1.26)0.81 (0.67, 0.99) E+P in OS/ E+P in CT0.93 (0.64, 1.36) E+P Hazard Ratio in the CT and OS as a Function of Time from Initiation of E+P Use Coronary Heart Disease

11. Difference in Distribution in Years from E+P Initiation between WHI Cohorts

12. Ratio of OS to CT Hazard Ratios for E+P Use

13. E+P Hazard Ratios (95% CIs) as a Function of Years from E+P Initiation, and Average HRs over Various Times from E+P Initiation, Assuming Common HR Functions in the CT and OS Years from Coronary Heart Disease Venous Thromboembolism E+P Initiation HR (95% CI) HR (95% CI) < 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) > 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 163:589-599,2006) 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 (E+P) Clinical Trials and Corresponding Observational Study Samples Years from Hormone E-aloneE+P Treatment Initiation HR (95% CI) Coronary Heart Disease < 2 1.11 (0.73, 1.69)1.58 (1.12, 2.24) 2 - 5 1.17 (0.88, 1.56)1.19 (0.87, 1.63) > 5 0.81 (0.62, 1.06)0.86 (0.59, 1.26) Hormone Therapy: HR in OS/HR in CT0.89 (0.67, 1.19)0.93 (0.64, 1.36) Stroke < 2 1.48 (0.89, 2.44)1.41 (0.90, 2.22) 2 - 5 1.18 (0.83, 1.67)1.14 (0.82, 1.59) > 5 1.48 (1.06, 2.06)1.12 (0.73, 1.72) Hormone Therapy: HR in OS/HR in CT0.68 (0.48, 0.97)0.76 (0.49, 1.18) Venous Thromboembolism < 2 2.18 (1.15, 4.13)3.02 (1.94, 4.69) 2 - 5 1.22 (0.80, 1.85)1.85 (1.30, 2.65) > 5 1.06 (0.72, 1.56)1.47 (0.96, 2.24) Hormone Therapy: HR in OS/HR in CT0.82 (0.54, 1.23)0.84 (0.55, 1.28)

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* *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, O’Sullivan MJ, Anderson G (2007). To appear, AJE (E-alone). Revised for AJE (E+P). † Age-adjusted to the 5-year age distribution in the CT cohort. PlaceboE-aloneRatioControlE-aloneRatio Number of Women5,429 5,31014,45821,663 Mean age63.6 65.363.0 Mean years of follow-up7.1 7.07.2 Number of Events133 104380626 Age-Adjusted † Annualized Incidence (%)0.35 0.280.800.360.411.13 Placebo E+PRatioControl E+PRatio Number of Women8,102 8,50632,75517,382 Mean age63.3 63.264.761.1 Mean years of follow-up5.6 5.55.6 Number of Events150 199610583 Age-Adjusted † Annualized Incidence (%)0.33 0.421.250.330.651.94 CTOS CTOS E+P E-alone

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).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, …).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, …).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.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.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)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 trialsHigh-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 (p<0.0001) 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 Goals: Intervention Group Photos courtesy of USDA Agricultural Research Service 20% energy from fat 5 or more fruit and vegetable servings daily 6 or more grain servings daily

32. Mean (SD) of Nutrient Consumption by Randomization Group *Difference significant at p<0.001 from a two sample t-test

33. Comparison of Cancer Incidence Rates between Intervention and Comparison Groups in the Women’s 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.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.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 Women’s Health Initiative Nutritional Biomarkers Study

37. Measurement Models for Nutritional Epidemiology (Carroll, Freedman, Kaaks, Kipnis, Spiegelman, Rosner, Prentice…) Recovery Biomarkers: X biomarker = Z + e W self-report = a 0 + a 1 Z + a 2 V + a 3 ZV + r + ε 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: X biomarker = b 0 + b 1 Z + 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 E+P women Compare baseline to 1-year serum proteome in pools of size 10

41. CONTROL CANCER Immuno-depletion (top six proteins) Concentration, buffer exchange and labeling SAMPLES MIXED ANION EXCHANGE CHROMATOGRAPHY (12 pools) REVERSE-PHASE CHROMATOGRAPHY (10-12 fractions / AEX) SAMPLE A Light Acrylamide SAMPLE A Light Acrylamide SAMPLE B Heavy Acrylamide ( 13 Cx3=3Da shift) SAMPLE B Heavy Acrylamide ( 13 Cx3=3Da shift) Reduction with DTT and Alkylation 120-150 SHOTGUN LC-MS/MS CONTROL CANCER Immuno-depletion (top six proteins) Concentration, buffer exchange and labeling SAMPLES MIXED ANION EXCHANGE CHROMATOGRAPHY (12 pools) REVERSE-PHASE CHROMATOGRAPHY (10-12 fractions / AEX) SAMPLE A Light Acrylamide SAMPLE A Light Acrylamide SAMPLE B Heavy Acrylamide ( 13 Cx3=3Da shift) SAMPLE B Heavy Acrylamide ( 13 Cx3=3Da shift) Reduction with DTT and Alkylation 120-150 SHOTGUN LC-MS/MS CONTROL CANCER Immuno-depletion (top six proteins) Concentration, buffer exchange and labeling SAMPLES MIXED ANION EXCHANGE CHROMATOGRAPHY (12 pools) REVERSE-PHASE CHROMATOGRAPHY (10-12 fractions / AEX) SAMPLE A Light Acrylamide SAMPLE A Light Acrylamide SAMPLE B Heavy Acrylamide ( 13 Cx3=3Da shift) SAMPLE B Heavy Acrylamide ( 13 Cx3=3Da shift) Reduction with DTT and Alkylation 120-150 SHOTGUN LC-MS/MS CONTROL CANCER Immuno-depletion (top six proteins) Concentration, buffer exchange and labeling SAMPLES MIXED ANION EXCHANGE CHROMATOGRAPHY (12 pools) REVERSE-PHASE CHROMATOGRAPHY (10-12 fractions / AEX) SAMPLE A Light Acrylamide SAMPLE A Light Acrylamide SAMPLE B Heavy Acrylamide ( 13 Cx3=3Da shift) SAMPLE B Heavy Acrylamide ( 13 Cx3=3Da shift) Reduction with DTT and Alkylation 120-150 SHOTGUN LC-MS/MS Baseline HT 1yr Immunodepletion (top six proteins) Concentration, buffer exchange and labeling SAMPLES MIXED ANION EXCHANGE CHROMATOGRAPHY (12 pools) REVERSE-PHASE CHROMATOGRAPHY (11 fractions / AEX) SAMPLE A Light Acrylamide SAMPLE A Light Acrylamide SAMPLE B Heavy Acrylamide ( 13 Cx3=3Da shift) SAMPLE B Heavy Acrylamide ( 13 Cx3=3Da shift) Reduction with DTT and Alkylation 12 x 11= 132 Fractions/IPAS SHOTGUN LC-MS/MS 5 E+P: 132 x 5=660 5 E: 132 x 5=660 Total: 1,320 fractions IPAS Faca V et al., J. Proteome Res., 5, 2006, 2009-2018 Faca V et al., J. Proteome Res., 2007 accepted “Quantitative analysis of acrylamide labeled serum proteins by LC-MS/MS” “Contribution of protein fractionation to depth of analysis of the serum and plasma proteomes”

42. Data acquisition from 5 million mass spectra

43. 698 E+PE 9521,054 Protein quant_common

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 enterpriseAn enhanced preventive intervention development enterprise Observational studies of maximal reliability for promising intervention conceptsObservational studies of maximal reliability for promising intervention concepts Full-scale intervention trials when rationale strong enough, and public health potential sufficiently greatFull-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)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 evaluationIdentify preventive interventions that merit initial testing or full-scale evaluation Receive and evaluate preventive trial proposalsReceive and evaluate preventive trial proposals Identify and facilitate needed methodologic researchIdentify and facilitate needed methodologic research Group Composition Population, basic and clinical scientistsPopulation, basic and clinical scientists Leaders in key areas for intervention developmentLeaders in key areas for intervention development Leaders in major chronic disease research areasLeaders in major chronic disease research areas Representatives from within and outside of NIHRepresentatives from within and outside of NIH