Advertisement

Research strategies for assessing epidemiolgic associations, in relation to the distribution and measurement of exposures

  • Ross L. Prentice
Conference paper

Abstract

It seems important to distinguish epidemiologic effects that are small because the exposure (or characteristic) is unimportant for the study disease from effects that are small by virtue of study design and execution. In the former situation there is little variation in disease risk across the exposure levels that are within the range of common human experience and it follows that only a small fraction of current human disease burden can be attributed to the particular exposure. In the latter situation, a careful scrutiny of potential study populations, study designs, and exposure assessment instruments is needed to optimise the reliability and efficiency of research to assess the exposure-disease association.

Keywords

Breast Cancer Postmenopausal Breast Cancer Percent Energy Exposure Distribution Epidemiologic Effect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tannenbaum A (1942) Genesis and growth of tumors. III. Effects of a high fat diet. Cancer Res. 2: 468–475Google Scholar
  2. 2.
    Newberne PM, Shrager TE, Conner MW (1989) Experimental evidence on the nutritional prevention of cancer. In: Moon TE, Micozzi MS (eds) Nutrition and cancer prevention: Investigating the role of micronutrients. New York: Marcel Dekker: 33–82Google Scholar
  3. 3.
    Freedman L, Clifford C, Messina MW (1990) Analysis of dietary fat, calories, body weight, and the development of mammary tumors in rats and mice: A review. Cancer Res 50: 5710–5719PubMedGoogle Scholar
  4. 4.
    Carroll RJ, Ruppert D, Stefanski LA (1995) Measurement error in nonlinear models. Chapman and Hall, New YorkGoogle Scholar
  5. 5.
    Prentice RL, Sheppard L (1990) Dietary fat and cancer: consistency of the epidemiologic data, and disease prevention that may follow from a practical reduction in fat consumption. Cancer Causes and Control 1: 81–97PubMedCrossRefGoogle Scholar
  6. 6.
    Howe GR, Hirohata T, Hislop G, Iscovich JM, Yuan JM, Katsonyanni K, Lubin F, Marubini E, Modan B, Rohan T, Toniolo P, Shunzhang Y (1990) Dietary factors and risk of breast cancer: combined analysis of 12 case-control studies. J Natl Cancer Inst 82: 561–569PubMedCrossRefGoogle Scholar
  7. 7.
    Hunter DJ, Spiegelman D, Adami HO, Beeson L, van den Brandt PA, Folsom AR, Fraser GE, Goldbohm A, Graham S, Howe GR, Kushi LH, Marshall JR, MdDermott A, Miller AB, Spiezer FE, Wolk A, Yuan SS, Willett W (1996) Cohort studies of fat intake and the risk of breast cancer–a pooled analysis. New Engl J Med 334: 356–361PubMedCrossRefGoogle Scholar
  8. 8.
    Rosner B, Willett WC, Spiegelman D (1989) Correction of logistic regression relative risk estimates and confidence intervals for systematic within-person measurement error. Stat Med 8: 1051–1069PubMedCrossRefGoogle Scholar
  9. 9.
    Rosner B, Spiegelman D, Willett WC (1990) Correction of logistic regression relative risk estimates and confidence intervals for measurement error: the case of multiple covariates measured with error. Am J Epidemiol 132: 734–745PubMedGoogle Scholar
  10. 10.
    Wacholder S, Armstrong B, Hartge P (1993) Validation studies using an alloyed gold standard. Am J Epidemiol 137: 1251–1258PubMedGoogle Scholar
  11. 11.
    Carroll RJ, Ruppert D, Stefanski LA (1995) Measurement error in nonlinear models. Chapman and Hall, New YorkGoogle Scholar
  12. 12.
    Plummer M, Clayton D (1993) Measurement error in dietary assessment: an assessment using covariance structured models. Part II Stat Med 12: 937–948Google Scholar
  13. 13.
    Lichtman SW, Pisarska K, Berman ER, Pestone M, Dowling H, Offenbacker E, Weisel H, Heshka S, Matthews DE, Heymsfield SB (1992) Discrepancy between self-reported and actual calorie intake and exercise in obese subjects. N Engl J Med 327: 1893–1898PubMedCrossRefGoogle Scholar
  14. 14.
    Bandini LG, Schoeller DA, Cyr HN, Dietz WH (1990) Validity of reported energy intake in obese and non-obese adolescents. Am J Clin Nutr 52: 421–425PubMedGoogle Scholar
  15. 15.
    Heitman BL, Lessner L (1995) Dietary underreporting by obese individuals–is it specific or non-specific. Br Med J 331: 986–989CrossRefGoogle Scholar
  16. 16.
    Martin LJ, Su W, Jones PJ, Lockwood GA, Tritchler DL, Boyd NF (1996) Comparison of energy intakes determined by food records and doubly labeled water in women participating in a dietary intervention trial. Am J Clin Nutr 63: 483–490PubMedGoogle Scholar
  17. 17.
    Sawaya AL, Tucker K, Tsay R, Willett WC, Saltzman E, Dallai GE, Roberts SB (1996) Evaluation of four methods for determining energy intake in young and older women: comparison with doubly labeled water measurements of total energy expenditure. Am J Clin Nutr 63: 491–499PubMedGoogle Scholar
  18. 18.
    Prentice RL (1996) Measurement error and results from analytic epidemiology: Dietary fat and breast cancer. In press. J Natl Cancer InstGoogle Scholar
  19. 19.
    Insull W, Henderson MM, Prentice RL, Thompson DJ, Clifford C, Goldman S, Gorbach S, Moskowitz M, Thompson R, Woods M (1990) Results of a feasibility study of a low-fat diet. Arch Intern Med 150: 421–427PubMedCrossRefGoogle Scholar
  20. 20.
    Henderson MM, Kushi LH, Thompson DJ, Gorbach SL, Clifford LK, Insull W, Moskowitz M, Thompson RS (1990) Feasibility of a randomised trial of a low-fat diet for the prevention of breast cancer: Dietary compliance in the Women’s Health Trial Vanguard Study. Prey Med 19: 115–133CrossRefGoogle Scholar
  21. 21.
    Willett WC, Sampson L, Stampfer MJ, Rosner B, Bain C, Witschi J, Hennekens CH, Spiezer F (1985) Reproducibility and validity of a semiquantitative food frequency questionnaire. Am J Epid 122: 51–65Google Scholar
  22. 22.
    Block G, Hartman AM, Dresser CM, Carroll MD, Gannon J, Gardner LA (1986) Data-based approach to diet questionnaire design and testing. Am J Epidemiol 124: 453–496PubMedGoogle Scholar
  23. 23.
    Rossouw JE, Finnegan CP, Harlan WR, Pinn VW, Clifford C, McGowan JA (1995) The evaluation of the Women’s Health Initiative: Perspectives from the NIH. J Am Med Women’s Assoc 50: 50–55Google Scholar
  24. 24.
    Prentice RL, Sheppard L (1995) Aggregate data studies of disease risk factors. Biometrika 82: 113–125CrossRefGoogle Scholar
  25. 25.
    Sheppard L, Prentice RL (1995) On the reliability and precision of within and between population estimates of relative risk parameters. Biometrics 51: 853–863PubMedCrossRefGoogle Scholar
  26. 26.
    Sheppard L, Prentice RL, Rossing MA (1996) Design considerations for estimation of exposure effects on disease risk, using aggregate data studies. To appear, Statist in MedGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Ross L. Prentice
    • 1
  1. 1.SeattleUSA

Personalised recommendations