References
- 1 Aalen, O. (1978). Nonparametric estimation of partial transition probabilities in multiple decrement models, Annals of Statistics 6, 534–545.
- 2 Aalen, O. & Johansen, S. (1978). An empirical transition matrix for nonhomogeneous Markov chains based on censored observations, Scandinavian Journal of Statistics 5, 141–150.
- 3 American Cancer Society (1992). Cancer Facts and Figures. ACS, Atlanta.
- 4 American Society of Human Genetics Ad Hoc Committee (1994). Statement of the American Society of Human Genetics on Genetic Testing for Breast and Ovarian Cancer Predisposition, American Journal of Human Genetics 55, i–iv.
- 5
Andersen, P. K. &
Gill, R. D.
(1982).
Cox's regression models for counting processes: a large-sample study,
Annals of Statistics
4,
1100–1120.
10.1214/aos/1176345976 Google Scholar
- 6
Andersen, P. K.,
Borgan, Ø,
Gill, R. D. &
Keiding, N.
(1993).
Statistical Models Based on Counting Processes.
Springer-Verlag,
New York.
10.1007/978-1-4612-4348-9 Google Scholar
- 7
Anderson, D. E. &
Badzioch, M. D.
(1985).
Risk of familial breast cancer,
Cancer
56,
383–387.
10.1002/1097-0142(19850715)56:2<383::AID-CNCR2820560230>3.0.CO;2-0 CAS PubMed Web of Science® Google Scholar
- 8 Anderson, S. J., Ahnn, S. & Duff, K. (1992). NSABP Breast Cancer Prevention Trial Risk Assessment Program, Version 2, University of Pittsburgh Department of Biostatistics, Pittsburgh.
- 9 Baker, L. H. (1982). Breast cancer detection demonstration project: five-year summary report, CA Cancer Journal for Clinicians 32, 194–225.
- 10 Benichou, J. (1993). A computer program for estimating individualized probabilities of breast cancer, Computers and Biomedical Research 26, 373–382.
- 11 Benichou, J. (1995). A complete analysis of variability of absolute risk from a population-based case–control study on breast cancer, Biometrical Journal 37, 3–24.
- 12 Benichou, J. & Gail, M. H. (1989). A delta-method for implicitly defined random variables, American Statistician 43, 41–44.
- 13 Benichou, J. & Gail, M. H. (1990). Estimates of absolute cause-specific risk in cohort studies, Biometrics 46, 813–826.
- 14 Benichou, J. & Gail, M. H. (1995). Methods of inference for estimates of absolute risk derived from population-based case–control studies, Biometrics 51, 182–194.
- 15 Benichou, J. & Wacholder, S. (1994). A comparison of three approaches to estimate exposure-specific incidence rates from population-based case–control data, Statistics in Medicine 13, 651–661.
- 16
Benichou, J.,
Byrne, C. &
Gail, M. H.
(1997).
An approach to estimating exposure-specific rates of breast cancer from a two-stage case–control study within a cohort,
Statistics in Medicine
16,
133–151.
10.1002/(SICI)1097-0258(19970130)16:2<133::AID-SIM476>3.0.CO;2-C CAS PubMed Web of Science® Google Scholar
- 17 Benichou, J., Gail, M. H. & Mulvihill, J. J. (1996). Graphs to estimate an individualized risk of breast cancer, Journal of Clinical Oncology 14, 103–110.
- 18 Berkson, J. (1958). Smoking and lung cancer: some observations on two recent reports, Journal of the American Statistical Association 53, 28–38.
- 19 Biesecker, B. B., Boehnke, M., Calzone, K., Markel, D. S., Garber, J. E., Collins, F. S. & Weber, B. L. (1993). Genetic counseling for families with inherited susceptibility to breast and ovarian cancer, Journal of the American Medical Association 269, 1970–1974.
- 20 Bondy, M. L., Lustbader, E. D., Halabi, S., Ross, E. & Vogel, V. G. (1994). Validation of a breast cancer risk assessment model in women with a positive family history, Journal of the National Cancer Institute 86, 620–625.
- 21 Borgan, Ø., Goldstein, L. & Langholz, B. (1995). Methods for the analysis of sampled cohort data in the Cox proportional hazards model, Annals of Statistics 23, 1749–1778.
- 22 Breslow, N. E. & Cain, K. C. (1988). Logistic regression for two-stage case–control data, Biometrika 75, 11–20.
- 23 Breslow, N. E. & Day, N. E. (1987). Statistical Methods in Cancer Research, Vol. II: The Design and Analysis of Cohort Studies. IARC Scientific Publications 82, Lyon.
- 24 Breslow, N. E., Lubin, J. H., Marek, P. & Langholz, B. (1983). Multiplicative models and cohort analysis, Journal of the American Statistical Association 78, 1–12.
- 25 Bruzzi, P., Green, S. B., Byar, D. P., Brinton, L. A. & Schairer, C. (1985). Estimating the population attributable risk for multiple risk factors using case–control data, American Journal of Epidemiology 122, 904–914.
- 26 Chang, I., Gelman, R. & Pagano, M. (1982). Corrected group prognostic curves and summary statistics, Journal of Chronic Diseases 35, 669–674.
- 27 Chiang, C. L. (1961). A stochastic study of the life table and its applications: III. The follow-up study with the consideration of competing risks, Biometrics 17, 57–58.
- 28 Chiang, C. L. (1968). Introduction to Stochastic Processes in Biostatistics. Wiley, New York.
- 29 Cornfield, J. (1951). A method for estimating comparative rates from clinical data: applications to cancer of the lung, breast and cervix, Journal of the National Cancer Institute 11, 1269–1275.
- 30 Cornfield, J. (1956). A statistical problem arising from retrospective studies, in Proceedings of the Third Berkeley Symposium, Vol. IV, J. Neyman, ed. University of California Press, Monterey, pp. 133–148.
- 31
Cox, D. R.
(1972).
Regression models and lifetables (with discussion),
Journal of the Royal Statistical Society, Series B
34,
187–220.
10.1111/j.2517-6161.1972.tb00899.x Google Scholar
- 32 Cox, D. R. (1975). Partial likelihood, Biometrika 62, 269–276.
- 33 Cutler, S. J. & Ederer, F. (1958). Maximum utilization of the life table method in analyzing survival, Journal of Chronic Diseases 8, 699–712.
- 34 Dorey, F. J. & Korn, E. L. (1987). Effective sample sizes for confidence intervals for survival probabilities, Statistics in Medicine 6, 679–687.
- 35 Dupont, D. W. (1989). Converting relative risks to absolute risks: a graphical approach, Statistics in Medicine 8, 641–651.
- 36 Easton, D. F., Peto, J. & Babiker, A. G. (1991). Floating absolute risk: an alternative to relative risk in survival and case–control analysis avoiding an arbitrary reference group, Statistics in Medicine 10, 1025–1035.
- 37 Efron, B. (1986). How biased is the apparent error rate of a prediction rule?, Journal of the American Statistical Association 81, 461–470.
- 38 Elandt-Johnson, R. C. (1977). Various estimators of conditional probabilities of death in follow-up studies. Summary of results, Journal of Chronic Diseases 30, 247–256.
- 39 Elveback, L. (1958). Estimation of survivorship in chronic disease: the “actuarial” method, Journal of the American Statistical Association 53, 420–440.
- 40 Flanders, W. D. & Greenland, S. (1991). Analytic methods for two-stage case–control studies and other stratified designs, Statistics in Medicine 10, 739–747.
- 41 Fleiss, J. L., Dunner, D. L., Stallone, F. & Fieve, R. R. (1976). The life table: a method for analyzing longitudinal studies, Archives of General Psychiatry 33, 107–112.
- 42 Gail, M. H. (1975). Measuring the benefit of reduced exposure to environmental carcinogens, Journal of Chronic Diseases 28, 135–147.
- 43 Gail, M. H. (1975). A review and critique of some models used in competing risk analysis, Biometrics 31, 209–222.
- 44 Gail, M. H. & Benichou, J. (1992). Assessing the risk of breast cancer in individuals, in Cancer Prevention, V. T. Vita & S. A. Rosenberg, eds. Lippincott, Philadelphia, pp. 1–15.
- 45 Gail, M. H. & Benichou, J. (1994). Validation studies on a model for breast cancer risk (editorial), Journal of the National Cancer Institute 86, 573–575.
- 46 Gail, M. H. & Byar, D. P. (1986). Variance calculations for direct adjusted survival curves, with applications to testing for no treatment effect, Biometrical Journal 28, 587–599.
- 47 Gail, M. H., Brinton, L. A., Byar, D. P., Corle, D. K., Green, S. B., Schairer, C. & Mulvihill, J. J. (1989). Projecting individualized probabilities of developing breast cancer for white females who are being examined annually, Journal of the National Cancer Institute 81, 1879–1886.
- 48
Gail, M. H.,
Eagan, R. T.,
Feld, R.,
Ginsberg, R.,
Godell, B.,
Hill, L.,
Holmes, E. C.,
Lubeman, J. M.,
Mountain, C. F.,
Oldham, R. K.,
Pearson, F. G.,
Wright, P. W.,
Lake, W. H., and the
Lung Cancer Study Group
(1984).
Prognostic factors in patients with resected stage I non-small-cell lung cancer. A report from the Lung Cancer Study Group,
Cancer
54,
1802–1813.
10.1002/1097-0142(19841101)54:9<1802::AID-CNCR2820540908>3.0.CO;2-4 CAS PubMed Web of Science® Google Scholar
- 49 Gray, R. J. (1988). A class of k-sample tests for comparing the cumulative incidence of a competing risk, Annals of Statistics 16, 1141–1151.
- 50 Greenland, S. (1981). Multivariate estimation of exposure-specific incidence from case–control studies, Journal of Chronic Diseases 34, 445–453.
- 51 Hartge, P., Cahill, J. J., West, D., Hauck, M., Austin, D., Silverman, D. & Hoover, R. (1985). Design and methods in a multicenter case–control interview study, American Journal of Public Health 74, 52–56.
- 52 Holford, T. R. (1980). The analysis of rates and of survivorship using log-linear models, Biometrics 36, 299–305.
- 53 Hoskins, K. F., Stopfer, J. E., Calzone, K. A., Merajver, S. D., Rebbeck, T. R., Garber, J. E. & Weber, B. L. (1995). Assessment and counseling for women with a family history of breast cancer: a guide for clinicians, Journal of the American Medical Association 273, 577–585.
- 54 Kalbfleisch, J. D. & Lawless, J. F. (1988). Likelihood analysis of multi-stage models for disease incidence and mortality, Statistics in Medicine 7, 149–160.
- 55 Kalbfleisch, J. D. & Prentice, R. L. (1980). The Statistical Analysis of Failure Time Data. Wiley, New York.
- 56 Kaplan, E. L. & Meier, P. (1958). Nonparametric estimation from incomplete observations, Journal of the American Statistical Association 53, 457–481.
- 57 Kay, R. & Schumacher, M. (1983). Unbiased assessment of treatment effects on disease recurrence and survival in clinical trials, Statistics in Medicine 2, 41–58.
- 58 Keiding, N. & Andersen, P. K. (1989). Nonparametric estimation of transition intensities and transition probabilities: a case study of a two-state Markov process, Applied Statistics 38, 319–329.
- 59 Kleinbaum, D. G., Kupper, L. L. & Morgenstern, H. (1982). Epidemiologic Research: Principles and Quantitative Methods. Lifetime Learning Publications, Belmont.
- 60 Korn, E. L. & Dorey, F. J. (1992). Applications of crude incidence curves, Statistics in Medicine 11, 813–829.
- 61 Laird, N. & Oliver, D. (1981). Covariance analysis of censored survival data using log-linear analysis techniques, Journal of the American Statistical Association 76, 231–240.
- 62 Langholz, B. & Borgan, Ø. (1997). Estimation of absolute risk from nested case–control data, Biometrics 53, 767–774.
- 63 Levin, M. L. (1953). The occurrence of lung cancer in man, Acta Unio Internationalis Contra Cancrum 9, 531–541.
- 64 Liang, K. -Y. & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models, Biometrika 73, 13–22.
- 65 Liddell, J. C., McDonald, J. C. & Thomas, D. C. (1977). Methods of cohort analysis: appraisal by application to asbestos mining (with discussion), Journal of the Royal Statistical Society, Series A 140, 469–491.
- 66 Littell, A. S. (1952). Estimation of the t-year survival rate from follow-up studies over a limited period of time, Human Biology 24, 87–116.
- 67 Little, R. J. A. & Rubin, D. B. (1987). Statistical Analysis with Missing Data. Wiley, New York.
- 68 MacMahon, B. (1962). Prenatal X-ray exposure and childhood cancer, Journal of the National Cancer Institute 28, 1173–1191.
- 69 MacMahon, B. & Pugh, T. F. (1970). Epidemiology: Principles and Methods. Little, Brown & Company, Boston.
- 70 Makuch, R. W. (1982). Adjusted survival curve estimation using covariates, Journal of Chronic Diseases 35, 437–443.
- 71 Mantel, N. (1973). Synthetic retrospective studies and related topics, Biometrics 29, 479–486.
- 72 Markush, R. E. (1977). Levin's attributable risk statistic for analytic studies and vital statistics, American Journal of Epidemiology 105, 401–406.
- 73 Matthews, D. E. (1988). Likelihood-based confidence intervals for functions of many parameters, Biometrika 75, 139–144.
- 74 Mausner, J. S. & Bahn, A. K. (1974). Epidemiology: An Introductory Text. W.B. Saunders, Philadelphia.
- 75 Miettinen, O. S. (1974). Proportion of disease caused or prevented by a given exposure, trait or intervention, American Journal of Epidemiology 99, 325–332.
- 76 Miettinen, O. S. (1976). Estimability and estimation in case–referent studies, American Journal of Epidemiology 103, 226–235.
- 77 Morgenstern, H., Kleinbaum, D. G. & Kupper, L. L. (1980). Measures of disease incidence used in epidemiologic research, International Journal of Epidemiology 9, 97–104.
- 78 Mulvihill, J. J., Safyer, A. W. & Bening, J. K. (1982). Prevention in familial breast cancer: counseling and prophylactic mastectomy, Preventive Medicine 11, 500–511.
- 79 Murphy, V. K. & Haywood, L. J. (1981). Survival analysis by sex, age group and hemotype in sickle cell disease, Journal of Chronic Diseases 34, 313–319.
- 80 Neutra, R. R. & Drolette, M. E. (1978). Estimating exposure-specific disease rates from case–control studies using Bayes' theorem, American Journal of Epidemiology 108, 214–222.
- 81 Oakes, D. (1981). Survival times: aspects of partial likelihood (with discussion), International Statistical Review 49, 235–264.
- 82 Ottman, R., King, M. C., Pike, M. C. & Henderson, B. E. (1983). Practical guide for estimating risk for familial breast cancer, Lancet 2, 556–558.
- 83 Prentice, R. L. (1986). A case–cohort design for epidemiologic cohort studies and disease prevention trials, Biometrika 73, 1–11.
- 84 Prentice, R. L. & Breslow, N. E. (1978). Retrospective studies and failure time models, Biometrika 65, 153–158.
- 85 Prentice, R. L. & Pyke, R. (1979). Logistic disease incidence models and case–control studies, Biometrika 66, 403–411.
- 86 Prentice, R. L., Kalbfleisch, J. D., Peterson, A. V., Flournoy, N., Farewell, V. T. & Breslow, N. E. (1978). The analysis of failure times in the presence of competing risks, Biometrics 34, 541–554.
- 87 Rao, C. R. (1965). Linear Statistical Inference and Its Application. Wiley, New York, pp. 319–322.
- 88 Reilly, M. & Pepe, M. S. (1995). A mean score method for missing and auxiliary covariate data in regression models, Biometrika 82, 299–314.
- 89 Robins, J. M., Rotnitzky, A. & Zhao, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed, Journal of the American Statistical Association 89, 846–866.
- 90 Schill, W., Jöckel, K-H., Drescher, K. & Timm, J. (1993). Logistic analysis in case–control studies under validation sampling, Biometrika 80, 339–352.
- 91
Schlesselman, J. J.
(1982).
Case–Control Studies: Design, Conduct and Analysis.
Oxford University Press,
New York.
10.1021/bi00268a044 Google Scholar
- 92 Schrag, D., Kuntz, K. M., Garber, J. E. & Weeks, J. C. (1997). Decision analysis—effects of prophylactic mastectomy and oophorectomy on life expectancy among women with BRCA1 or BRCA2 mutations, New England Journal of Medicine 336, 1465–1471.
- 93 Smigel, K. (1992). Breast cancer prevention trial takes off, Journal of the National Cancer Institute 84, 669–670.
- 94 Spiegelman, D., Colditz, G. A., Hunter, D. & Hertzmark, E. (1994). Validation of the Gail et al. model for predicting individual breast cancer risk, Journal of the National Cancer Institute 86, 600–607.
- 95 Tsiatis, A. A. (1981). A large-sample study of Cox's regression model, Annals of Statistics 9, 93–108.
- 96 Walter, S. D. (1976). The estimation and interpretation of attributable risk in health research, Biometrics 32, 829–849.
- 97 Walter, S. D. (1980). Prevention for multifactorial diseases, American Journal of Epidemiology 112, 409–416.
- 98 Weinberg, C. R. & Wacholder, S. (1990). The design and analysis of case–control studies with biased sampling, Biometrics 46, 963–975.
- 99 White, J. E. (1982). A two-stage design for the study of the relationship between a rare exposure and a rare disease, American Journal of Epidemiology 115, 119–128.
Citing Literature
