Estimating the Absolute Risk of Disease Associated with Identified Mutations

  • Mitchell H. Gail
  • Nilanjan Chatterjee


For a given mutation status, we define the absolute risk as the chance that disease develops in a defined age interval, given that the person is well at the beginning of the interval. Absolute risk is reduced by competing risks of mortality, that may cause the person to die before the disease of interest develops. We distinguish absolute risk from the pure cumulative risk of disease that is often estimated in the genetic epidemiologic literature, and we concentrate on estimating marginal risks for members selected at random from the population, rather than family specific risks. We review cohort, population-based case–control, case–control family, and kin-cohort designs for estimating absolute and pure cumulative risks associated with a measurable genetic mutation.


Breast Cancer Breast Cancer Risk Absolute Risk Natl Cancer Inst Cohort Design 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anderson P, Borgen O, Gill R, Keiding N (1991) Statistical Models based on counting processes. Springer-Verlag, New YorkGoogle Scholar
  2. 2.
    Begg C (2002) On the use of familial aggregation in population-based case probands for calculating penetrance. J Natl Cancer Inst 94:1221–1226PubMedGoogle Scholar
  3. 3.
    Benichou J, Gail M (1990) Estimates of absolute cause-specific risk in cohort studies. Biometrics 46:813–826CrossRefPubMedGoogle Scholar
  4. 4.
    Benichou J, Gail M (1995) Methods of inference for estimates of absolute risk derived from population-based case-control studies. Biometrics 51:182–194CrossRefPubMedGoogle Scholar
  5. 5.
    Chatterjee N, Wacholder S (2001) A marginal likelihood approach for estimating penetrance from kin-cohort designs. Biometrics 57:245–252CrossRefPubMedGoogle Scholar
  6. 6.
    Chatterjee N, Hartge P, Wacholder S (2003) Adjustment for competing risk in kin-cohort estimation. Genetic Epidemiol 25:303–313CrossRefGoogle Scholar
  7. 7.
    Chatterjee N, Kalaylioglu Z, Shih J, Gail M (2006) Case-control and case-only designs with genotype and family history data: estimating relative risk, residual familial aggregation, and cumulative risk. Biometrics 62:36–48CrossRefPubMedGoogle Scholar
  8. 8.
    Claus E, Risch N, Thompson W (1991) Genetic analysis of breast cancer in the cancer and steroid hormone study. Am J Human Genetics 48:232–242Google Scholar
  9. 9.
    Claus E, Risch N, Thompson W (1994) Autosomal dominant inheritance of early-onset breast cancer. Implications and risk prediction. Cancer 73:643–651Google Scholar
  10. 10.
    Clayton DG (1978) A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65:141–151CrossRefGoogle Scholar
  11. 11.
    Cornfield J (1951) A method of estimating comparative rates from clinical data; applications to cancer of the lung, breast, and cervix. J Natl Cancer Inst 11:1269–1275PubMedGoogle Scholar
  12. 12.
    Ford D, Easton D, Stratton M, Narod S, Goldar D, Devilee P, Bishop D, Weber B, Lenoir G, Chang-Claude J, et al (1998) Genetic heterogeneity and penetrance analysis of the brca1 and brca2 genes in breast cancer families. the breast cancer linkage consortium. Am J Human Genetics 62:676–689CrossRefGoogle Scholar
  13. 13.
    Gail M, Chatterjee N (2004) Some biases that may affect kin-cohort studies for estimating the risks from identified disease genes. Springer, New YorkGoogle Scholar
  14. 14.
    Gail M, Brinton L, Byar D, Corle D, Green S, Schairer C, Mulvihill J (1989) Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 81:1879–1886CrossRefPubMedGoogle Scholar
  15. 15.
    Gail M, Constantino J, Bryant J, Croyle R, Freedman L, Helzlsouer K, Vogel V (1999a) Weighing the risks and benefits of tamoxifen treatment for preventing breast cancer. J Natl Cancer Inst 91:1829–1846CrossRefPubMedGoogle Scholar
  16. 16.
    Gail M, Pee D, Benichou J, Carroll R (1999b) Designing studies to estimate the penetrance of an identified autosomal dominant mutation: cohort, case-control, and genotyped-proband designs. Genetic Epidemiol 16:15–39CrossRefGoogle Scholar
  17. 17.
    Gail M, Pee D, Carroll R (1999c) Kin-cohort designs for gene characterization. J Natl Cancer Inst Monogr 26:55–60PubMedGoogle Scholar
  18. 18.
    Gail M, Pee D, Carroll R (2001) Effects of violations of assumptions on likelihood methods for estimating the penetrance of an autosomal dominant mutation from kin-cohort studies. J Stat Plan Infer 96:167–177CrossRefGoogle Scholar
  19. 19.
    Genest C, Mackay R (1986) The joy of copulas: bivariate distributions with given marginals. Am Stat 40:280–283CrossRefGoogle Scholar
  20. 20.
    Hooper J, Southey M, Dite G, Jolley D, Giles G, McGredie M, Venter DED (1990) Population-based estimate of the average age-specific cumulative risk of breast cancer for a defined set of protein-truncating mutations in brca1 and brca2. Australian breast cancer family study. Am J Human Genetics 8:813–826Google Scholar
  21. 21.
    Hsu L, Prentice R, Zhao L, Fan J (1999) On dependence estimation using correlated failure time data from case-control family studies. Biometrika 86:743–753CrossRefGoogle Scholar
  22. 22.
    Langholz B, Goldstein L (1996) Estimation of absolute risk from nested case-control data. Biometrics 53:767–774CrossRefGoogle Scholar
  23. 23.
    Li H (1998) Analysis of age of onset data from case-control family studies. Biometrics 54:1030–1039CrossRefPubMedGoogle Scholar
  24. 24.
    Liang K, Zeger S, Qaqish B (1992) Multivariate regression-analyses for categorical-data. J Royal Stat Soc Ser B 54:3–40Google Scholar
  25. 25.
    Moore D, Chatterjee N, Pee D, Gail M (2001) Pseudo-likelihood estimates of the cumulative risk of an autosomal dominant disease from a kin-cohort study. Genetic Epidemioly 20: 210–227CrossRefGoogle Scholar
  26. 26.
    Oakes D (1989) Bivariate survival models induced by frailties. J Am Stat Assoc 84:487–493CrossRefGoogle Scholar
  27. 27.
    Prentice R, Pyke R (1979) Logistic disease incidence models and case-control studies. Biometrika 66:403–411CrossRefGoogle Scholar
  28. 28.
    Prentice R, Kalbfleisch J, Jr AP, Flournoy N, Farewell V, Breslow N (1978) The analysis of failure times in the presence of competing risks. Biometrics 34:541–554CrossRefPubMedGoogle Scholar
  29. 29.
    Self S, Prentice R (1988) Asymptotic distribution theory and efficiency results for case-cohort studies. Ann Statistics 16:64–81CrossRefGoogle Scholar
  30. 30.
    Shih J, Chatterjee N (2002) Analysis of survival data from case-control family studies. Biometrics 54:1115–1128CrossRefGoogle Scholar
  31. 31.
    Struewing J, Hartge P, Wacholder S, Baker S, Berlin M, McAdams M, Timmerman M, Brody L, Tucker M (1997) The risk of cancer associated with specific mutations of brca1 and brca2 among ashkenazi jews. New Engl J Med 336:1401–1408CrossRefPubMedGoogle Scholar
  32. 32.
    Wacholder S, Hartge P, Struewing J, Pee D, McAdams M, Brody L, Tucker M (1998) The kin-cohort study for estimating penetrance. Am J Epidemiol 148:623–630PubMedCrossRefGoogle Scholar
  33. 33.
    Whittemore A (1995) Logistic regression of family data from case-control studies. Biometrika 82:57–67CrossRefGoogle Scholar
  34. 34.
    Zhao L, Hsu L, Holte S, Chen Y, Quiaoit F, Prentice R (1998) Combined association and aggregation analysis of data from case-control family studies. Biometrika 85:299–315CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Mitchell H. Gail
    • 1
  • Nilanjan Chatterjee
    • 1
  1. 1.Biostatistics Branch, Division of Cancer Epidemiology and GeneticsNational Cancer Institute NIH, DHHSRockvilleUSA

Personalised recommendations