Real-time individual predictions of prostate cancer recurrence using joint models
Identifieur interne : 001915 ( Pmc/Checkpoint ); précédent : 001914; suivant : 001916Real-time individual predictions of prostate cancer recurrence using joint models
Auteurs : Jeremy M. G. Taylor [États-Unis] ; Yongseok Park [États-Unis] ; Donna P. Ankerst [Allemagne, États-Unis] ; Cecile Proust-Lima [France] ; Scott Williams [Australie] ; Larry Kestin [États-Unis] ; Kyoungwha Bae [États-Unis] ; Tom Pickles [Canada] ; Howard Sandler [États-Unis]Source :
- Biometrics [ 0006-341X ] ; 2013.
Abstract
Patients who were previously treated for prostate cancer with radiation therapy are monitored at regular intervals using a laboratory test called Prostate Specific Antigen (PSA). If the value of the PSA test starts to rise, this is an indication that the prostate cancer is more likely to recur, and the patient may wish to initiate new treatments. Such patients could be helped in making medical decisions by an accurate estimate of the probability of recurrence of the cancer in the next few years. In this paper, we describe the methodology for giving the probability of recurrence for a new patient, as implemented on a web-based calculator. The methods use a joint longitudinal survival model. The model is developed on a training dataset of 2,386 patients and tested on a dataset of 846 patients. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the risk of recurrence that uses the longitudinal PSA measures from a new patient.
Url:
DOI: 10.1111/j.1541-0420.2012.01823.x
PubMed: 23379600
PubMed Central: 3622120
Affiliations:
- Allemagne, Australie, Canada, France, États-Unis
- Bavière, Californie, District de Haute-Bavière, Michigan, Texas
- Munich
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PMC:3622120Le document en format XML
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<front><div type="abstract" xml:lang="en"><title>Summary</title>
<p id="P1">Patients who were previously treated for prostate cancer with radiation therapy are monitored at regular intervals using a laboratory test called Prostate Specific Antigen (PSA). If the value of the PSA test starts to rise, this is an indication that the prostate cancer is more likely to recur, and the patient may wish to initiate new treatments. Such patients could be helped in making medical decisions by an accurate estimate of the probability of recurrence of the cancer in the next few years. In this paper, we describe the methodology for giving the probability of recurrence for a new patient, as implemented on a web-based calculator. The methods use a joint longitudinal survival model. The model is developed on a training dataset of 2,386 patients and tested on a dataset of 846 patients. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the risk of recurrence that uses the longitudinal PSA measures from a new patient.</p>
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<title-group><article-title>Real-time individual predictions of prostate cancer recurrence using joint models</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Taylor</surname>
<given-names>Jeremy M. G.</given-names>
</name>
<xref ref-type="aff" rid="A1">1</xref>
<xref rid="FN1" ref-type="author-notes">*</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Park</surname>
<given-names>Yongseok</given-names>
</name>
<xref ref-type="aff" rid="A1">1</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Ankerst</surname>
<given-names>Donna P.</given-names>
</name>
<xref ref-type="aff" rid="A2">2</xref>
<xref ref-type="aff" rid="A3">3</xref>
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<contrib contrib-type="author"><name><surname>Proust-Lima</surname>
<given-names>Cecile</given-names>
</name>
<xref ref-type="aff" rid="A4">4</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Williams</surname>
<given-names>Scott</given-names>
</name>
<xref ref-type="aff" rid="A5">5</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Kestin</surname>
<given-names>Larry</given-names>
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<xref ref-type="aff" rid="A6">6</xref>
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<contrib contrib-type="author"><name><surname>Bae</surname>
<given-names>Kyoungwha</given-names>
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<xref ref-type="aff" rid="A7">7</xref>
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<contrib contrib-type="author"><name><surname>Pickles</surname>
<given-names>Tom</given-names>
</name>
<xref ref-type="aff" rid="A8">8</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Sandler</surname>
<given-names>Howard</given-names>
</name>
<xref ref-type="aff" rid="A9">9</xref>
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<aff id="A1"><label>1</label>
Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109</aff>
<aff id="A2"><label>2</label>
Zentrum Mathematik, Technische Universitaet Muenchen, Munich, Germany</aff>
<aff id="A3"><label>3</label>
Dept. of Epidemiology/Biostatistics and Urology, University of Texas Health Science Center at San Antonio, San Antonio, Texas</aff>
<aff id="A4"><label>4</label>
INSERM, U897, Epidemiology and Biostatistics Research Center, 33076 Bordeaux, France</aff>
<aff id="A5"><label>5</label>
Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia</aff>
<aff id="A6"><label>6</label>
Michigan Healthcare Professionals/21st Century Oncology, Farmington Hills, MI 48334</aff>
<aff id="A7"><label>7</label>
Alcon Laboratories Novartis Company, Fort Worth, TX</aff>
<aff id="A8"><label>8</label>
British Columbia Cancer Agency, Vancouver, BC, Canada</aff>
<aff id="A9"><label>9</label>
Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA</aff>
<author-notes><corresp id="FN1"><label>*</label>
<email>jmgt@umich.edu</email>
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<pub-date pub-type="nihms-submitted"><day>4</day>
<month>10</month>
<year>2012</year>
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<pub-date pub-type="epub"><day>04</day>
<month>2</month>
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<pub-date pub-type="pmc-release"><day>10</day>
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<year>2013</year>
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<volume>69</volume>
<issue>1</issue>
<fpage>206</fpage>
<lpage>213</lpage>
<abstract><title>Summary</title>
<p id="P1">Patients who were previously treated for prostate cancer with radiation therapy are monitored at regular intervals using a laboratory test called Prostate Specific Antigen (PSA). If the value of the PSA test starts to rise, this is an indication that the prostate cancer is more likely to recur, and the patient may wish to initiate new treatments. Such patients could be helped in making medical decisions by an accurate estimate of the probability of recurrence of the cancer in the next few years. In this paper, we describe the methodology for giving the probability of recurrence for a new patient, as implemented on a web-based calculator. The methods use a joint longitudinal survival model. The model is developed on a training dataset of 2,386 patients and tested on a dataset of 846 patients. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the risk of recurrence that uses the longitudinal PSA measures from a new patient.</p>
</abstract>
<kwd-group><kwd>Joint longitudinal-survival model</kwd>
<kwd>Online calculator</kwd>
<kwd>Predicted probability</kwd>
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<kwd>PSA</kwd>
</kwd-group>
<funding-group><award-group><funding-source country="United States">National Cancer Institute : NCI</funding-source>
<award-id>R21 CA110518 || CA</award-id>
</award-group>
<award-group><funding-source country="United States">National Cancer Institute : NCI</funding-source>
<award-id>R01 CA129102 || CA</award-id>
</award-group>
<award-group><funding-source country="United States">National Cancer Institute : NCI</funding-source>
<award-id>P50 CA069568 || CA</award-id>
</award-group>
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</front>
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<name sortKey="Park, Yongseok" sort="Park, Yongseok" uniqKey="Park Y" first="Yongseok" last="Park">Yongseok Park</name>
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<country name="Allemagne"><region name="Bavière"><name sortKey="Ankerst, Donna P" sort="Ankerst, Donna P" uniqKey="Ankerst D" first="Donna P." last="Ankerst">Donna P. Ankerst</name>
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