Grant Number:	5U01CA088177-05 Interpret this number
Primary Investigator:	Yakovlev, Andrei
Organization:	University Of Rochester
Project Title:	Mechanistic Modeling of Breast Cancer Surveillance
Fiscal Year:	2003

The general objective of this project is to consider the utility of mechanistic models of tumor development and detection in analysis of the impact of breast cancer screening in population- based settings. A stochastic model of cancer screening we propose offers the following distinct advantages: 1. It provides a simple but still realistic description of cancer latency; 2. It can be generalized in various ways while retaining its basic structure; 3. It furnishes a biologically meaningful interpretation of data analyses; 4. It accommodates standard population-based statistical data; its implementation does not depend heavily on availability of the data yielded by screening trials; 5. Rigorous statistical methods are available for estimating model parameters; 6. It can be used for designing optimal strategies of cancer screening and surveillance. The model will be validated with data on breast cancer from the Utah Population Data Base and the Utah Cancer Registry. Using these resources we will obtain initial parameter values for a pertinent estimation algorithm designed for grouped data on breast cancer mortality provided by the National Center for Health Statistics. This two-step estimation procedure will be tested by computer simulations and analyses of epidemiological data. In addition, we will explore the utility of stochastic approximation techniques in estimation of model parameters within the microsimulation framework.




Introduction to the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Cancer Models.
Authors: Alagoz O. , Berry D.A. , de Koning H.J. , Feuer E.J. , Lee S.J. , Plevritis S.K. , Schechter C.B. , Stout N.K. , Trentham-Dietz A. , Mandelblatt J.S. , et al. .
Source: Medical Decision Making : An International Journal Of The Society For Medical Decision Making, 2018 Apr; 38(1_suppl), p. 3S-8S.
PMID: 29554472
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Identifiability of the joint distribution of age and tumor size at detection in the presence of screening.
Authors: Hanin L. , Yakovlev A. .
Source: Mathematical Biosciences, 2007 Aug; 208(2), p. 644-57.
PMID: 17303192
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The University of Rochester model of breast cancer detection and survival.
Authors: Hanin L.G. , Miller A. , Zorin A.V. , Yakovlev A.Y. .
Source: Journal Of The National Cancer Institute. Monographs, 2006; (36), p. 66-78.
PMID: 17032896
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Effect of screening and adjuvant therapy on mortality from breast cancer.
Authors: Berry D.A. , Cronin K.A. , Plevritis S.K. , Fryback D.G. , Clarke L. , Zelen M. , Mandelblatt J.S. , Yakovlev A.Y. , Habbema J.D. , Feuer E.J. , et al. .
Source: The New England Journal Of Medicine, 2005-10-27 00:00:00.0; 353(17), p. 1784-92.
PMID: 16251534
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Multivariate distributions of clinical covariates at the time of cancer detection.
Authors: Hanin L.G. , Yakovlev A.Y. .
Source: Statistical Methods In Medical Research, 2004 Dec; 13(6), p. 457-89.
PMID: 15587434
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Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models.
Authors: Tsodikov A.D. , Ibrahim J.G. , Yakovlev A.Y. .
Source: Journal Of The American Statistical Association, 2003-12-01 00:00:00.0; 98(464), p. 1063-1078.
PMID: 21151838
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Testing goodness of fit for stochastic models of carcinogenesis.
Authors: Gregori G. , Hanin L. , Luebeck G. , Moolgavkar S. , Yakovlev A. .
Source: Mathematical Biosciences, 2002 Jan; 175(1), p. 13-29.
PMID: 11779625
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Modeling cancer detection: tumor size as a source of information on unobservable stages of carcinogenesis.
Authors: Bartoszy¿ski R. , Edler L. , Hanin L. , Kopp-Schneider A. , Pavlova L. , Tsodikov A. , Zorin A. , Yakovlev A.Y. .
Source: Mathematical Biosciences, 2001 Jun; 171(2), p. 113-42.
PMID: 11395047
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