The long-term objective of our research is to develop a statistical framework that will be used to translate the research findings to cancer prevention, diagnosis and management practices. The specific objective of this proposal is to develop a general framework for constructing risk prediction models (RPM's) for breast cancer that incorporates both environmental and genetic factors as well as family history, in the absence of genetic markers. The proposal has four specific aims: 1) to develop a general framework for constructing RPMs by incorporating known risk factors such as candidate genes, other biological markers, demographic variables, birth history, diet, history of medications, medical history and other lifestyle variables, which can be either time- independent or time dependent; 2) to develop a general framework for constructing RPMs by incorporating, (in addition to known risk factors described above), family history data as well as family data collected from family members; 3) to outline protocols on how to use such programs and to develop computer programs for all these new methodologies with a """"""""user friendly"""""""" interface, which will be disseminated to the scientific community via the Internet; 4) to develop RPMs for breast cancer based on the data sets collected in the Breast Cancer Detection Demonstration Program and Cancer Steroid Hormone study (among other available data sets in the Fred Hutchinson Cancer Research Center), to compare the RPMs that are constructed on different data sets and to validate them across study populations. Further, we will attempt to validate the refined RPM for breast cancer via ongoing studies in the Center. This development is rooted in recent advances made in statistics, epidemiology, genetics and genetic epidemiology. Since most of the risk factors are varying with age and outcomes are generally ages of onset, the proportional hazard model has been generalized to include the birth history and two-stage models, and is chosen as a basic model for constructing RPMs.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA076466-04
Application #
6346046
Study Section
Project Start
2000-09-30
Project End
2002-09-29
Budget Start
1997-10-01
Budget End
1998-09-30
Support Year
4
Fiscal Year
2000
Total Cost
$71,191
Indirect Cost
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
Wick, David; Self, Steven G (2004) On simulating strongly-interacting, stochastic population models. Math Biosci 187:1-20
Wick, David; Self, Steven G (2004) On simulating strongly interacting, stochastic population models. II. Multiple compartments. Math Biosci 190:127-43
de Gunst, Mathisca C M; Dewanji, Anup; Luebeck, E Georg (2003) Exploring heterogeneity in tumour data using Markov chain Monte Carlo. Stat Med 22:1691-707
Curtis, S B; Luebeck, E G; Hazelton, W D et al. (2002) A new perspective of carcinogenesis from protracted high-LET radiation arises from the two-stage clonal expansion model. Adv Space Res 30:937-44
Wick, David; Self, Steven G (2002) What's the matter with HIV-directed killer T cells? J Theor Biol 219:19-31
Luebeck, E Georg; Moolgavkar, Suresh H (2002) Multistage carcinogenesis and the incidence of colorectal cancer. Proc Natl Acad Sci U S A 99:15095-100
Gregori, Giovanni; Hanin, Leonid; Luebeck, Georg et al. (2002) Testing goodness of fit for stochastic models of carcinogenesis. Math Biosci 175:13-29
Hazelton, W D; Luebeck, E G; Heidenreich, W F et al. (2001) Analysis of a historical cohort of Chinese tin miners with arsenic, radon, cigarette smoke, and pipe smoke exposures using the biologically based two-stage clonal expansion model. Radiat Res 156:78-94
Curtis, S B; Luebeck, E G; Hazelton, W D et al. (2001) The role of promotion in carcinogenesis from protracted high-LET exposure. Phys Med 17 Suppl 1:157-60
Wick, D; Self, S G (2000) Early HIV infection in vivo: branching-process model for studying timing of immune responses and drug therapy. Math Biosci 165:115-34

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