Regression calibration in nutritional epidemiology: Example of fat density and total energy in relationship to postmenopausal breast cancer

American Journal of Epidemiology

Volumn 178, Issue 11, 2013, Pages 1663-1672

  Prentice, Ross L ^a   Pettinger, Mary ^a   Tinker, Lesley F ^a   Huang, Ying ^a   Thomson, Cynthia A ^b   Johnson, Karen C ^c   Beasley, Jeannette ^d   Anderson, Garnet ^a   Shikany, James M ^e   Chlebowski, Rowan T ^f   Neuhouser, Marian L ^a  

^a Fred Hutchinson Cancer Research Center   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

^c UNIVERSITY OF TENNESSEE HEALTH SCIENCE CENTER   (United States)

^d ALBERT EINSTEIN COLLEGE OF MEDICINE   (United States)

^e University of Alabama at Birmingham   (United States)

^f UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bias; Biological markers; Breast cancer; Dietary assessment; Dietary energy; Dietary fat; Postmenopausal women

Indexed keywords

BIOLOGICAL MARKER;

BIOMARKER; BODY MASS; CALIBRATION; CANCER; CONFIDENCE INTERVAL; EPIDEMIOLOGY; FAT; HAZARD; REGRESSION;

ADULT; AGED; ARTICLE; BODY MASS; BREAST CANCER; CALIBRATION; COHORT ANALYSIS; CONTROLLED STUDY; DENSITY; ENERGY CONSUMPTION; FAT DENSITY; FAT INTAKE; FEMALE; FOOD; FOOD FREQUENCY QUESTIONNAIRE; FOUR DAY FOOD RECORD; HAZARD RATIO; HUMAN; INCIDENCE; MAJOR CLINICAL STUDY; MEASUREMENT ERROR; MEDICAL RECORD; NOISE; NUTRITIONAL ASSESSMENT; OBSERVATIONAL STUDY; POSTMENOPAUSE; REGRESSION CALIBRATION; SELF REPORT;

BIAS; BIOLOGICAL MARKERS; BREAST CANCER; DIETARY ASSESSMENT; DIETARY ENERGY; DIETARY FAT; POSTMENOPAUSAL WOMEN;

AGED; BIOLOGICAL MARKERS; BODY MASS INDEX; BREAST NEOPLASMS; CALIBRATION; CASE-CONTROL STUDIES; DATA INTERPRETATION, STATISTICAL; DIET RECORDS; DIET SURVEYS; DIETARY FATS; ENERGY INTAKE; FEMALE; FOLLOW-UP STUDIES; HUMANS; INCIDENCE; LINEAR MODELS; MIDDLE AGED; POSTMENOPAUSE; PROSPECTIVE STUDIES; QUESTIONNAIRES; RISK ASSESSMENT; SELF REPORT; UNITED STATES;

UNITED STATES;

EID: 84890068866     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwt198     Document Type: Article

References (31)

1. Freedman LS, Clifford C, Messina M. Analysis of dietary fat, calories, body weight, and the development of mammary tumors in rats and mice: A review. Cancer Res. 1990; 50(18):5710-5719.
2. Prentice RL, Sheppard L. Dietary fat and cancer: Consistency of the epidemiologic data, and disease prevention that may follow from a practical reduction in fat consumption. Cancer Causes Control. 1990;1(1):81-97.
3. Howe G, Hirohata T, Hislop T, et al. Dietary factors and risk of breast cancer: Combined analysis of 12 case-control studies. J Natl Cancer Inst. 1990;82(7):561-569.
4. Hunter D, Spiegelman D, Adami H-O, et al. Cohort studies of fat intake and the risk of breast cancer - a pooled analysis. N Engl J Med. 1996;334(6):356-361.
5. Prentice RL, Caan B, Chlebowski RT, et al. Low-fat dietary pattern and risk of invasive breast cancer: The Women's Health Initiative randomized controlled Dietary Modification Trial. JAMA. 2006;295(6):629-642.
6. Women's Health Initiative Study Group. Design of the Women's Health Initiative clinical trial and observational study. Control Clin Trials. 1998;19(1):61-109.
7. Martin LJ, Li Q, Melnichouk O, et al. A randomized trial of dietary intervention for breast cancer prevention. Cancer Res. 2011;71(1):123-133.
8. Chlebowski RT, Blackburn GL, Thomson CA, et al. Dietary fat reduction and breast cancer outcome: Interim efficacy results from theWomen's Intervention Nutrition Study. J Natl Cancer Inst. 2006;98(24):1767-1776.
9. Neuhouser ML, Tinker L, Shaw PA, et al. Use of recovery biomarkers to calibrate nutrient consumption self-reports in the Women's Health Initiative. Am J Epidemiol. 2008;167(10): 1247-1259.
10. Prentice RL, Mossavar-Rahmani Y, Huang Y, et al. Evaluation and comparison of food records, recalls and frequencies for energy and protein assessment using recovery biomarkers. Am J Epidemiol. 2011;174(5):591-603.
11. Prentice RL, Shaw PA, Bingham SA, et al. Biomarkercalibrated energy and protein consumption and increased cancer risk among postmenopausal women. Am J Epidemiol. 2009;169(8):977-989.
12. Prentice RL, Huang Y, Kuller LH, et al. Biomarker-calibrated energy and protein consumption and cardiovascular disease risk among postmenopausal women. Epidemiology. 2011; 22(2):170-179.
13. Prentice RL, Huang Y. Measurement error modeling and nutritional epidemiology association analyses. Canadian J Stat. 2011;39(3):498-509.
14. Bingham SA, Luben R,Welch A, et al. Are imprecise methods obscuring a relation between fat and breast cancer Lancet. 2003;362(9379):212-214.
15. Freedman LS, Potischman N, Kipnis V, et al. A comparison of two dietary instruments for evaluating the fat-breast cancer relationship. Int J Epidemiol. 2006;35(4):1011-1021.
16. Key TJ, Appleby PN, Cairns BJ, et al. Dietary fat and breast cancer: Comparison of results from food diaries and food-frequency questionnaires in the UK Dietary Cohort Consortium. Am J Clin Nutr. 2011;94(4): 1043-1052.
17. Thiébaut AC, Kipnis V, Chang SC, et al. Dietary fat and postmenopausal invasive breast cancer in the National Institutes of Health-AARP Diet and Health Study cohort. J Natl Cancer Inst. 2007;99(6):451-462.
18. Sieri S, Krogh V, Ferrari P, et al. Dietary fat and breast cancer risk in the European Prospective Investigation into Cancer and Nutrition. Am J Clin Nutr. 2008;88(5):1304-1312.
19. Park SY, Kolonel LN, Henderson BE, et al. Dietary fat and breast cancer in postmenopausal women according to ethnicity and hormone receptor status: The Multiethnic Cohort Study. Cancer Prev Res (Phila). 2012;5(2):216-228.
20. Freedman LS, Schatzkin A, Midthune D, et al. Dealing with dietary measurement error in nutritional cohort studies. J Natl Cancer Inst. 2011;103(14):1086-1092.
21. Hays J, Hunt J, Hubbell A, et al. TheWomen's Health Initiative recruitment methods and results. Ann Epidemiol. 2003;13(9 suppl):18S-77S.
22. Langer R, White E, Lewis C, et al. The WHI Observational Study: Baseline characteristics of participants and reliability of baseline measures. Ann Epidemiol. 2003;13(9 suppl): 107S-121S.
23. Patterson RE, Kristal AR, Carter RA, et al. Measurement characteristics of theWomen's Health Initiative food frequency questionnaire. Ann Epidemiol. 1999;9(3): 178-187.
24. Curb JD, McTiernan A, Heckbert SR, et al. Outcomes ascertainment and adjudication methods in theWomen's Health Initiative. Ann Epidemiol. 2003;13(9 suppl): 122S-128S.
25. Gail MH, Constantino JP, Bryant J, et al.Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer. J Natl Cancer Inst. 1999;91(21):1829-1846.
26. Cox DR. Regression models and life-tables. J R Stat Soc B. 1972;34(2):187-220.
27. Prentice RL. Covariate measurement errors and parameter estimation in a failure time regression model. Biometrika. 1982;69(2):331-342.
28. Rosner B, Spiegelman D,Willett WC. Correction of logistic regression relative risk estimates and confidence intervals for measurement error: The case of multiple covariates measured with error. Am J Epidemiol. 1990;132(4):734-745.
29. Wang CY, Hsu L, Feng ZD, et al. Regression calibration in failure time regression with surrogate variables. Biometrics. 1997;53(1):131-145.
30. Carroll RJ, Ruppert D, Stefanski LA, et al. Measurement Error in Nonlinear Models, a Modern Perspective. 2nd ed. Boca Raton, FL: Chapman and Hall/CRC; 2006.
31. Shaw PA, Prentice RL. Hazard ratio estimation for biomarkercalibrated dietary exposures. Biometrics. 2012;68(2): 397-407.