Modeling recovery curves with application to prostatectomy
In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We pro...
Gespeichert in:
| Veröffentlicht in: | Biostatistics (Oxford, England) England), 2019-10, Vol.20 (4), p.549-564 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 564 |
|---|---|
| container_issue | 4 |
| container_start_page | 549 |
| container_title | Biostatistics (Oxford, England) |
| container_volume | 20 |
| creator | Wang, Fulton Rudin, Cynthia Mccormick, Tyler H Gore, John L |
| description | In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature. |
| doi_str_mv | 10.1093/biostatistics/kxy002 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2036787035</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2036787035</sourcerecordid><originalsourceid>FETCH-LOGICAL-c353t-681b1160ccbabee0ff101ac2302271731b912f20025921d08fe003e6c605e5f03</originalsourceid><addsrcrecordid>eNpVkMtOwzAQRS0EoqXwBwhlySZ0bCdOwg5VvKQiNrCOHGcMhqQJtlPI32PagsRqZjF3ztUh5JTCBYWCzyvTOS-9cd4oN3__GgHYHpnSRORxwtNsf7OncSKSZEKOnHsLB4wLfkgmrMgSKiCbksuHrsbGrF4ii6pbox0jNdg1uujT-NdI9n1jVKB0q8h3UW83TFS-a8djcqBl4_BkN2fk-eb6aXEXLx9v7xdXy1jxlPtY5LSiAaZUJStE0JoClYrx0CajGadVQZlmoVxaMFpDrhGAo1ACUkw18Bk53_4N9I8BnS9b4xQ2jVxhN7iSARdZnkGgzUiyPVWhqLOoy96aVtqxpFD-WCv_WSu31kLsbEcYqhbrv9CvJv4NPHFt5g</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2036787035</pqid></control><display><type>article</type><title>Modeling recovery curves with application to prostatectomy</title><source>Oxford University Press Journals All Titles (1996-Current)</source><source>MEDLINE</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Alma/SFX Local Collection</source><creator>Wang, Fulton ; Rudin, Cynthia ; Mccormick, Tyler H ; Gore, John L</creator><creatorcontrib>Wang, Fulton ; Rudin, Cynthia ; Mccormick, Tyler H ; Gore, John L</creatorcontrib><description>In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature.</description><identifier>ISSN: 1465-4644</identifier><identifier>EISSN: 1468-4357</identifier><identifier>DOI: 10.1093/biostatistics/kxy002</identifier><identifier>PMID: 29741607</identifier><language>eng</language><publisher>England</publisher><subject>Aged ; Bayes Theorem ; Decision Support Techniques ; Humans ; Male ; Middle Aged ; Models, Statistical ; Outcome Assessment, Health Care - statistics & numerical data ; Prostatectomy - adverse effects ; Prostatectomy - statistics & numerical data</subject><ispartof>Biostatistics (Oxford, England), 2019-10, Vol.20 (4), p.549-564</ispartof><rights>The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c353t-681b1160ccbabee0ff101ac2302271731b912f20025921d08fe003e6c605e5f03</citedby><cites>FETCH-LOGICAL-c353t-681b1160ccbabee0ff101ac2302271731b912f20025921d08fe003e6c605e5f03</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29741607$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Fulton</creatorcontrib><creatorcontrib>Rudin, Cynthia</creatorcontrib><creatorcontrib>Mccormick, Tyler H</creatorcontrib><creatorcontrib>Gore, John L</creatorcontrib><title>Modeling recovery curves with application to prostatectomy</title><title>Biostatistics (Oxford, England)</title><addtitle>Biostatistics</addtitle><description>In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature.</description><subject>Aged</subject><subject>Bayes Theorem</subject><subject>Decision Support Techniques</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Models, Statistical</subject><subject>Outcome Assessment, Health Care - statistics & numerical data</subject><subject>Prostatectomy - adverse effects</subject><subject>Prostatectomy - statistics & numerical data</subject><issn>1465-4644</issn><issn>1468-4357</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkMtOwzAQRS0EoqXwBwhlySZ0bCdOwg5VvKQiNrCOHGcMhqQJtlPI32PagsRqZjF3ztUh5JTCBYWCzyvTOS-9cd4oN3__GgHYHpnSRORxwtNsf7OncSKSZEKOnHsLB4wLfkgmrMgSKiCbksuHrsbGrF4ii6pbox0jNdg1uujT-NdI9n1jVKB0q8h3UW83TFS-a8djcqBl4_BkN2fk-eb6aXEXLx9v7xdXy1jxlPtY5LSiAaZUJStE0JoClYrx0CajGadVQZlmoVxaMFpDrhGAo1ACUkw18Bk53_4N9I8BnS9b4xQ2jVxhN7iSARdZnkGgzUiyPVWhqLOoy96aVtqxpFD-WCv_WSu31kLsbEcYqhbrv9CvJv4NPHFt5g</recordid><startdate>20191001</startdate><enddate>20191001</enddate><creator>Wang, Fulton</creator><creator>Rudin, Cynthia</creator><creator>Mccormick, Tyler H</creator><creator>Gore, John L</creator><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>20191001</creationdate><title>Modeling recovery curves with application to prostatectomy</title><author>Wang, Fulton ; Rudin, Cynthia ; Mccormick, Tyler H ; Gore, John L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c353t-681b1160ccbabee0ff101ac2302271731b912f20025921d08fe003e6c605e5f03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Aged</topic><topic>Bayes Theorem</topic><topic>Decision Support Techniques</topic><topic>Humans</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Models, Statistical</topic><topic>Outcome Assessment, Health Care - statistics & numerical data</topic><topic>Prostatectomy - adverse effects</topic><topic>Prostatectomy - statistics & numerical data</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Fulton</creatorcontrib><creatorcontrib>Rudin, Cynthia</creatorcontrib><creatorcontrib>Mccormick, Tyler H</creatorcontrib><creatorcontrib>Gore, John L</creatorcontrib><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>Biostatistics (Oxford, England)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Fulton</au><au>Rudin, Cynthia</au><au>Mccormick, Tyler H</au><au>Gore, John L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Modeling recovery curves with application to prostatectomy</atitle><jtitle>Biostatistics (Oxford, England)</jtitle><addtitle>Biostatistics</addtitle><date>2019-10-01</date><risdate>2019</risdate><volume>20</volume><issue>4</issue><spage>549</spage><epage>564</epage><pages>549-564</pages><issn>1465-4644</issn><eissn>1468-4357</eissn><abstract>In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature.</abstract><cop>England</cop><pmid>29741607</pmid><doi>10.1093/biostatistics/kxy002</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1465-4644 |
| ispartof | Biostatistics (Oxford, England), 2019-10, Vol.20 (4), p.549-564 |
| issn | 1465-4644 1468-4357 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2036787035 |
| source | Oxford University Press Journals All Titles (1996-Current); MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
| subjects | Aged Bayes Theorem Decision Support Techniques Humans Male Middle Aged Models, Statistical Outcome Assessment, Health Care - statistics & numerical data Prostatectomy - adverse effects Prostatectomy - statistics & numerical data |
| title | Modeling recovery curves with application to prostatectomy |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T16%3A43%3A28IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Modeling%20recovery%20curves%20with%20application%20to%20prostatectomy&rft.jtitle=Biostatistics%20(Oxford,%20England)&rft.au=Wang,%20Fulton&rft.date=2019-10-01&rft.volume=20&rft.issue=4&rft.spage=549&rft.epage=564&rft.pages=549-564&rft.issn=1465-4644&rft.eissn=1468-4357&rft_id=info:doi/10.1093/biostatistics/kxy002&rft_dat=%3Cproquest_cross%3E2036787035%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2036787035&rft_id=info:pmid/29741607&rfr_iscdi=true |