Testing the evolution process of prostate-specific antigen in early stage prostate cancer: what is the proper underlying model?
This paper empirically tests a model of stochastic evolutions of prostate‐specific antigen (PSA), a trigger for intervention in an early stage prostate cancer surveillance program. It conducts hypothesis testing of the Geometric Browning Motion model based on its attributes of independent increments...
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| Veröffentlicht in: | Statistics in medicine 2011-11, Vol.30 (25), p.3038-3049 |
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| creator | Prisman, Eliezer Z. Gafni, Amiram Finelli, Antonio |
| description | This paper empirically tests a model of stochastic evolutions of prostate‐specific antigen (PSA), a trigger for intervention in an early stage prostate cancer surveillance program. It conducts hypothesis testing of the Geometric Browning Motion model based on its attributes of independent increments and linearity of the variance in the increment length versus a wide range of stochastic and deterministic alternatives. These alternatives include the currently accepted deterministic growth model. The paper reports strong empirical evidence in favour of the Geometric Browning Motion model. A model that best describes PSA evolution is a prerequisite to the establishment of decision‐making criteria for abandoning active surveillance (i.e. a strategy that involves close monitoring) in early stage prostate cancer. Thus, establishing empirically the type of PSA process is a first step toward the identification of more accurate triggers for abandoning active surveillance and starting treatment while the chances of curing the disease are still high. Copyright © 2011 John Wiley & Sons, Ltd. |
| doi_str_mv | 10.1002/sim.4329 |
| format | Article |
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Med</addtitle><description>This paper empirically tests a model of stochastic evolutions of prostate‐specific antigen (PSA), a trigger for intervention in an early stage prostate cancer surveillance program. It conducts hypothesis testing of the Geometric Browning Motion model based on its attributes of independent increments and linearity of the variance in the increment length versus a wide range of stochastic and deterministic alternatives. These alternatives include the currently accepted deterministic growth model. The paper reports strong empirical evidence in favour of the Geometric Browning Motion model. A model that best describes PSA evolution is a prerequisite to the establishment of decision‐making criteria for abandoning active surveillance (i.e. a strategy that involves close monitoring) in early stage prostate cancer. Thus, establishing empirically the type of PSA process is a first step toward the identification of more accurate triggers for abandoning active surveillance and starting treatment while the chances of curing the disease are still high. Copyright © 2011 John Wiley & Sons, Ltd.</description><subject>active surveillance</subject><subject>Data Interpretation, Statistical</subject><subject>Databases, Factual - statistics & numerical data</subject><subject>Decision Making</subject><subject>Early Detection of Cancer - statistics & numerical data</subject><subject>Geometric Brownian Motion</subject><subject>Humans</subject><subject>Male</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>Ontario - epidemiology</subject><subject>prostate cancer</subject><subject>Prostate-Specific Antigen - metabolism</subject><subject>Prostatic Neoplasms - diagnosis</subject><subject>Prostatic Neoplasms - epidemiology</subject><subject>stochastic evolution of PSA</subject><issn>0277-6715</issn><issn>1097-0258</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kM1OGzEUha2Kqgm0Ek-AvKObAf_EY5sNQikkqZJ0UapK3ViO507idn7CeAbIilfHaUJ2rGzpfv58z0HolJILSgi7DL68GHCmP6A-JVomhAl1hPqESZmkkooeOg7hLyGUCiY_oR6jiqWpVn30cg-h9dUStyvA8FgXXevrCq-b2kEIuM6319DaFpKwBudz77CtWr-ECvsKg22KDY7zJRxA7GzloLnCTyvbYh_-q-NwDQ3uqgzii-2HZZ1Bcf0ZfcxtEeDL_jxBv-5u74fjZPpjNBneTBPHhdIJgwycFZwyCzEDARCpSJUS2YKClowvtOVC5mRAlZMUFMtEltMB15IL7iw_Qec7b1zkoYuZTemDg6KwFdRdMEpryimXKpJfd6SLeUIDuVk3vrTNxlBitm2b2LbZth3Rs720W5SQHcC3eiOQ7IAnX8DmXZH5OZnthXvehxaeD7xt_plUcinM7_nIDOffR7PZ-Jv5w18BYwmaIg</recordid><startdate>20111110</startdate><enddate>20111110</enddate><creator>Prisman, Eliezer Z.</creator><creator>Gafni, Amiram</creator><creator>Finelli, Antonio</creator><general>John Wiley & Sons, Ltd</general><scope>BSCLL</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20111110</creationdate><title>Testing the evolution process of prostate-specific antigen in early stage prostate cancer: what is the proper underlying model?</title><author>Prisman, Eliezer Z. ; Gafni, Amiram ; Finelli, Antonio</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3589-2edeca5312ae1520ee5656885db1e9723b9a357f0418c71e82d5df14397353ca3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>active surveillance</topic><topic>Data Interpretation, Statistical</topic><topic>Databases, Factual - statistics & numerical data</topic><topic>Decision Making</topic><topic>Early Detection of Cancer - statistics & numerical data</topic><topic>Geometric Brownian Motion</topic><topic>Humans</topic><topic>Male</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>Ontario - epidemiology</topic><topic>prostate cancer</topic><topic>Prostate-Specific Antigen - metabolism</topic><topic>Prostatic Neoplasms - diagnosis</topic><topic>Prostatic Neoplasms - epidemiology</topic><topic>stochastic evolution of PSA</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Prisman, Eliezer Z.</creatorcontrib><creatorcontrib>Gafni, Amiram</creatorcontrib><creatorcontrib>Finelli, Antonio</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Statistics in medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Prisman, Eliezer Z.</au><au>Gafni, Amiram</au><au>Finelli, Antonio</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Testing the evolution process of prostate-specific antigen in early stage prostate cancer: what is the proper underlying model?</atitle><jtitle>Statistics in medicine</jtitle><addtitle>Statist. Med</addtitle><date>2011-11-10</date><risdate>2011</risdate><volume>30</volume><issue>25</issue><spage>3038</spage><epage>3049</epage><pages>3038-3049</pages><issn>0277-6715</issn><eissn>1097-0258</eissn><abstract>This paper empirically tests a model of stochastic evolutions of prostate‐specific antigen (PSA), a trigger for intervention in an early stage prostate cancer surveillance program. It conducts hypothesis testing of the Geometric Browning Motion model based on its attributes of independent increments and linearity of the variance in the increment length versus a wide range of stochastic and deterministic alternatives. These alternatives include the currently accepted deterministic growth model. The paper reports strong empirical evidence in favour of the Geometric Browning Motion model. A model that best describes PSA evolution is a prerequisite to the establishment of decision‐making criteria for abandoning active surveillance (i.e. a strategy that involves close monitoring) in early stage prostate cancer. Thus, establishing empirically the type of PSA process is a first step toward the identification of more accurate triggers for abandoning active surveillance and starting treatment while the chances of curing the disease are still high. Copyright © 2011 John Wiley & Sons, Ltd.</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><pmid>21826698</pmid><doi>10.1002/sim.4329</doi><tpages>12</tpages></addata></record> |
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| ispartof | Statistics in medicine, 2011-11, Vol.30 (25), p.3038-3049 |
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| language | eng |
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| source | MEDLINE; Wiley Online Library All Journals |
| subjects | active surveillance Data Interpretation, Statistical Databases, Factual - statistics & numerical data Decision Making Early Detection of Cancer - statistics & numerical data Geometric Brownian Motion Humans Male Models, Biological Models, Statistical Ontario - epidemiology prostate cancer Prostate-Specific Antigen - metabolism Prostatic Neoplasms - diagnosis Prostatic Neoplasms - epidemiology stochastic evolution of PSA |
| title | Testing the evolution process of prostate-specific antigen in early stage prostate cancer: what is the proper underlying model? |
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