A Stochastic Model of Carcinogenesis and Tumor Size at Detection
This paper discusses the distribution of tumor size at detection derived within the framework of a new stochastic model of carcinogenesis. This distribution assumes a simple limiting form, with age at detection tending to infinity which is found to be a generalization of the distribution that arises...
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Veröffentlicht in: | Advances in applied probability 1997-09, Vol.29 (3), p.607-628 |
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description | This paper discusses the distribution of tumor size at detection derived within the framework of a new stochastic model of carcinogenesis. This distribution assumes a simple limiting form, with age at detection tending to infinity which is found to be a generalization of the distribution that arises in the length-biased sampling. Two versions of the model are considered with reference to spontaneous and induced carcinogenesis; both of them show similar asymptotic behavior. When the limiting distribution is applied to real data analysis its adequacy can be tested through testing the conditional independence of the size, V, and the age, A, at detection given A > t*, where the value of t* is to be estimated from the given sample. This is illustrated with an application to data on premenopausal breast cancer. The proposed distribution offers the prospect of the estimation of some biologically meaningful parameters descriptive of the temporal organization of tumor latency. An estimate of the model stability to the prior distribution of tumor size and some other stability results for the Bayes formula are given. |
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G. ; Rachev, S. T. ; Tsodikov, A. D. ; Yakovlev, A. Yu</creator><creatorcontrib>Hanin, L. G. ; Rachev, S. T. ; Tsodikov, A. D. ; Yakovlev, A. Yu</creatorcontrib><description>This paper discusses the distribution of tumor size at detection derived within the framework of a new stochastic model of carcinogenesis. This distribution assumes a simple limiting form, with age at detection tending to infinity which is found to be a generalization of the distribution that arises in the length-biased sampling. Two versions of the model are considered with reference to spontaneous and induced carcinogenesis; both of them show similar asymptotic behavior. When the limiting distribution is applied to real data analysis its adequacy can be tested through testing the conditional independence of the size, V, and the age, A, at detection given A > t*, where the value of t* is to be estimated from the given sample. This is illustrated with an application to data on premenopausal breast cancer. The proposed distribution offers the prospect of the estimation of some biologically meaningful parameters descriptive of the temporal organization of tumor latency. An estimate of the model stability to the prior distribution of tumor size and some other stability results for the Bayes formula are given.</description><identifier>ISSN: 0001-8678</identifier><identifier>EISSN: 1475-6064</identifier><identifier>DOI: 10.2307/1428079</identifier><identifier>CODEN: AAPBBD</identifier><language>eng</language><publisher>Cambridge, UK: Cambridge University Press</publisher><subject>Cancer ; Carcinogenesis ; Distribution ; Dosage ; General Applied Probability ; Lesions ; Mathematics ; Parametric models ; Perceptron convergence procedure ; Probability ; Statistical analysis ; Stochastic models ; Studies ; Topological theorems ; Tumors</subject><ispartof>Advances in applied probability, 1997-09, Vol.29 (3), p.607-628</ispartof><rights>Copyright © Applied Probability Trust 1997</rights><rights>Copyright 1997 Applied Probability Trust</rights><rights>Copyright Applied Probability Trust Sep 1997</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c311t-f10fbe12989a1c3928a8175ee2d478c16e7ebdd121866911aa8e3182875b2f273</citedby><cites>FETCH-LOGICAL-c311t-f10fbe12989a1c3928a8175ee2d478c16e7ebdd121866911aa8e3182875b2f273</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/1428079$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/1428079$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>315,782,786,805,834,27933,27934,58026,58030,58259,58263</link.rule.ids></links><search><creatorcontrib>Hanin, L. G.</creatorcontrib><creatorcontrib>Rachev, S. T.</creatorcontrib><creatorcontrib>Tsodikov, A. D.</creatorcontrib><creatorcontrib>Yakovlev, A. Yu</creatorcontrib><title>A Stochastic Model of Carcinogenesis and Tumor Size at Detection</title><title>Advances in applied probability</title><addtitle>Advances in Applied Probability</addtitle><description>This paper discusses the distribution of tumor size at detection derived within the framework of a new stochastic model of carcinogenesis. This distribution assumes a simple limiting form, with age at detection tending to infinity which is found to be a generalization of the distribution that arises in the length-biased sampling. Two versions of the model are considered with reference to spontaneous and induced carcinogenesis; both of them show similar asymptotic behavior. When the limiting distribution is applied to real data analysis its adequacy can be tested through testing the conditional independence of the size, V, and the age, A, at detection given A > t*, where the value of t* is to be estimated from the given sample. This is illustrated with an application to data on premenopausal breast cancer. The proposed distribution offers the prospect of the estimation of some biologically meaningful parameters descriptive of the temporal organization of tumor latency. An estimate of the model stability to the prior distribution of tumor size and some other stability results for the Bayes formula are given.</description><subject>Cancer</subject><subject>Carcinogenesis</subject><subject>Distribution</subject><subject>Dosage</subject><subject>General Applied Probability</subject><subject>Lesions</subject><subject>Mathematics</subject><subject>Parametric models</subject><subject>Perceptron convergence procedure</subject><subject>Probability</subject><subject>Statistical analysis</subject><subject>Stochastic models</subject><subject>Studies</subject><subject>Topological theorems</subject><subject>Tumors</subject><issn>0001-8678</issn><issn>1475-6064</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1997</creationdate><recordtype>article</recordtype><recordid>eNp90M9LwzAUB_AgCtYp_gtBBPFQzUvbJL05Nn_BxMPmuaTp68zYmplkB_3rrawgePD0ePB53wdfQs6B3fCMyVvIuWKyPCAJ5LJIBRP5IUkYY5AqIdUxOQlh1a-ZVCwhd2M6j8686xCtoS-uwTV1LZ1ob2znlthhsIHqrqGL3cZ5OrdfSHWkU4xoonXdKTlq9Trg2TBH5O3hfjF5Smevj8-T8Sw1GUBMW2BtjcBLVWowWcmVViALRN7kUhkQKLFuGuCghCgBtFaYgeJKFjVvucxG5GKfu_XuY4chViu3813_suIAor8roEdXe2S8C8FjW2293Wj_WQGrftqphnZ6ebmXqxCd_4ddD4F6U3vbLPH37V_7DRShbIc</recordid><startdate>199709</startdate><enddate>199709</enddate><creator>Hanin, L. G.</creator><creator>Rachev, S. T.</creator><creator>Tsodikov, A. D.</creator><creator>Yakovlev, A. Yu</creator><general>Cambridge University Press</general><general>Applied Probability Trust</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>199709</creationdate><title>A Stochastic Model of Carcinogenesis and Tumor Size at Detection</title><author>Hanin, L. G. ; Rachev, S. T. ; Tsodikov, A. D. ; Yakovlev, A. Yu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c311t-f10fbe12989a1c3928a8175ee2d478c16e7ebdd121866911aa8e3182875b2f273</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Cancer</topic><topic>Carcinogenesis</topic><topic>Distribution</topic><topic>Dosage</topic><topic>General Applied Probability</topic><topic>Lesions</topic><topic>Mathematics</topic><topic>Parametric models</topic><topic>Perceptron convergence procedure</topic><topic>Probability</topic><topic>Statistical analysis</topic><topic>Stochastic models</topic><topic>Studies</topic><topic>Topological theorems</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hanin, L. G.</creatorcontrib><creatorcontrib>Rachev, S. T.</creatorcontrib><creatorcontrib>Tsodikov, A. D.</creatorcontrib><creatorcontrib>Yakovlev, A. Yu</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Advances in applied probability</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hanin, L. G.</au><au>Rachev, S. T.</au><au>Tsodikov, A. D.</au><au>Yakovlev, A. Yu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Stochastic Model of Carcinogenesis and Tumor Size at Detection</atitle><jtitle>Advances in applied probability</jtitle><addtitle>Advances in Applied Probability</addtitle><date>1997-09</date><risdate>1997</risdate><volume>29</volume><issue>3</issue><spage>607</spage><epage>628</epage><pages>607-628</pages><issn>0001-8678</issn><eissn>1475-6064</eissn><coden>AAPBBD</coden><abstract>This paper discusses the distribution of tumor size at detection derived within the framework of a new stochastic model of carcinogenesis. This distribution assumes a simple limiting form, with age at detection tending to infinity which is found to be a generalization of the distribution that arises in the length-biased sampling. Two versions of the model are considered with reference to spontaneous and induced carcinogenesis; both of them show similar asymptotic behavior. When the limiting distribution is applied to real data analysis its adequacy can be tested through testing the conditional independence of the size, V, and the age, A, at detection given A > t*, where the value of t* is to be estimated from the given sample. This is illustrated with an application to data on premenopausal breast cancer. The proposed distribution offers the prospect of the estimation of some biologically meaningful parameters descriptive of the temporal organization of tumor latency. An estimate of the model stability to the prior distribution of tumor size and some other stability results for the Bayes formula are given.</abstract><cop>Cambridge, UK</cop><pub>Cambridge University Press</pub><doi>10.2307/1428079</doi><tpages>22</tpages></addata></record> |
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subjects | Cancer Carcinogenesis Distribution Dosage General Applied Probability Lesions Mathematics Parametric models Perceptron convergence procedure Probability Statistical analysis Stochastic models Studies Topological theorems Tumors |
title | A Stochastic Model of Carcinogenesis and Tumor Size at Detection |
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