Nonparametric inference on cause‐specific quantile residual life
Identifieur interne : 002751 ( Istex/Corpus ); précédent : 002750; suivant : 002752Nonparametric inference on cause‐specific quantile residual life
Auteurs : Jong-Hyeon Jeong ; Jason P. FineSource :
- Biometrical Journal [ 0323-3847 ] ; 2013-01.
English descriptors
- KwdEn :
- Axillary dissection, Biometrical, Biometrical journal, Breast cancer, Breast cancer patients, Breast deaths, Censoring, Censoring proportion, Error probabilities, Global test, Global test statistic, Gmbh, Jeong, Kgaa, Life expectancy, Life function, Life functions, Mastectomy, Median, Nodal group, Nominal level, Nonparametric, Nsabp, Nsabp data, Nsabp study, Operations research, Percentile, Probability density function, Quantile, Radical mastectomy, Residual, Residual life, Residual life distributions, Residual life subdistributions, Residual lifetimes, Scenario, Statistic, Subdistribution, Subdistributions, Survival data, Survival function, Test statistic, Time point, Total mastectomy, Verlag, Verlag gmbh, Weinheim, Weinheim biometrical journal.
- Teeft :
- Axillary dissection, Biometrical, Biometrical journal, Breast cancer, Breast cancer patients, Breast deaths, Censoring, Censoring proportion, Error probabilities, Global test, Global test statistic, Gmbh, Jeong, Kgaa, Life expectancy, Life function, Life functions, Mastectomy, Median, Nodal group, Nominal level, Nonparametric, Nsabp, Nsabp data, Nsabp study, Operations research, Percentile, Probability density function, Quantile, Radical mastectomy, Residual, Residual life, Residual life distributions, Residual life subdistributions, Residual lifetimes, Scenario, Statistic, Subdistribution, Subdistributions, Survival data, Survival function, Test statistic, Time point, Total mastectomy, Verlag, Verlag gmbh, Weinheim, Weinheim biometrical journal.
Abstract
In medical research, investigators are often interested in inferring time‐to‐event distributions under competing risks. It is well known, however, that the naive approach based on the Kaplan–Meier method to estimate the proportion of cause‐specific events overestimates the true quantity. In this paper, we show that the quantile residual life function, a natural and popular summary measure of survival data, could be also seriously affected by the competing events. An existing two‐sample test statistic for inference on median residual life is modified for competing risks data, which does not involve estimation of the improper probability density function of the subdistribution of cause‐specific events under censoring. Simulation results demonstrate that the test statistic controls the type 1 error probabilities reasonably well. The proposed method is applied to a real data example from a large‐scale phase III breast cancer study.
Url:
DOI: 10.1002/bimj.201100190
Links to Exploration step
ISTEX:A78BF5520AAF2DA7133DDCB263D72F956CBC011BLe document en format XML
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It is well known, however, that the naive approach based on the Kaplan–Meier method to estimate the proportion of cause‐specific events overestimates the true quantity. In this paper, we show that the quantile residual life function, a natural and popular summary measure of survival data, could be also seriously affected by the competing events. An existing two‐sample test statistic for inference on median residual life is modified for competing risks data, which does not involve estimation of the improper probability density function of the subdistribution of cause‐specific events under censoring. Simulation results demonstrate that the test statistic controls the type 1 error probabilities reasonably well. The proposed method is applied to a real data example from a large‐scale phase III breast cancer study.</abstract><qualityIndicators><refBibsNative>true</refBibsNative><abstractWordCount>137</abstractWordCount><abstractCharCount>941</abstractCharCount><keywordCount>5</keywordCount><score>8.644</score><pdfWordCount>5479</pdfWordCount><pdfCharCount>31883</pdfCharCount><pdfVersion>1.3</pdfVersion><pdfPageCount>14</pdfPageCount><pdfPageSize>595.218 x 790.789 pts</pdfPageSize></qualityIndicators><title>Nonparametric inference on cause‐specific quantile residual life</title><pmid><json:string>23065930</json:string></pmid><genre><json:string>article</json:string></genre><host><title>Biometrical Journal</title><language><json:string>unknown</json:string></language><doi><json:string>10.1002/(ISSN)1521-4036</json:string></doi><issn><json:string>0323-3847</json:string></issn><eissn><json:string>1521-4036</json:string></eissn><publisherId><json:string>BIMJ</json:string></publisherId><volume>55</volume><issue>1</issue><pages><first>68</first><last>81</last><total>14</total></pages><genre><json:string>journal</json:string></genre><subject><json:item><value>Research Paper</value></json:item></subject></host><namedEntities><unitex><date><json:string>2012-09-06</json:string><json:string>1079</json:string><json:string>3</json:string></date><geogName></geogName><orgName><json:string>National Surgical Adjuvant Breast and Bowel</json:string><json:string>USA Department of Biostatistics, University of North Carolina, Chapel Hill, NC</json:string></orgName><orgName_funder></orgName_funder><orgName_provider></orgName_provider><persName><json:string>J. 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(2009)</json:string><json:string>Jung et al., 2009</json:string><json:string>Su and Wei (1993)</json:string><json:string>Jeong and Fine, 2009</json:string><json:string>Kaplan and Meier, 1958</json:string><json:string>Maltoni et al., 2005</json:string><json:string>Jeong et al., 2008</json:string><json:string>Lutgers et al., 2009</json:string><json:string>Jeong and Fine (2009)</json:string><json:string>Gaynor et al., 1993</json:string><json:string>Arnold and Brockett, 1983</json:string></ref_bibl><bibl></bibl></unitex></namedEntities><ark><json:string>ark:/67375/WNG-G4MPMZ8Z-N</json:string></ark><categories><wos><json:string>1 - science</json:string><json:string>2 - statistics & probability</json:string><json:string>2 - mathematical & computational biology</json:string></wos><scienceMetrix><json:string>1 - natural sciences</json:string><json:string>2 - mathematics & statistics</json:string><json:string>3 - statistics & probability</json:string></scienceMetrix><scopus><json:string>1 - Social Sciences</json:string><json:string>2 - Decision Sciences</json:string><json:string>3 - Statistics, Probability and Uncertainty</json:string><json:string>1 - Health Sciences</json:string><json:string>2 - Medicine</json:string><json:string>3 - General Medicine</json:string><json:string>1 - Physical Sciences</json:string><json:string>2 - Mathematics</json:string><json:string>3 - Statistics and Probability</json:string></scopus></categories><publicationDate>2013</publicationDate><copyrightDate>2013</copyrightDate><doi><json:string>10.1002/bimj.201100190</json:string></doi><id>A78BF5520AAF2DA7133DDCB263D72F956CBC011B</id><score>1</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/A78BF5520AAF2DA7133DDCB263D72F956CBC011B/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/A78BF5520AAF2DA7133DDCB263D72F956CBC011B/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/A78BF5520AAF2DA7133DDCB263D72F956CBC011B/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main">Nonparametric inference on cause‐specific quantile residual life</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>Wiley Publishing Ltd</publisher><availability><licence>© 2012 WILEY‐VCH Verlag GmbH & Co. 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It is well known, however, that the naive approach based on the Kaplan–Meier method to estimate the proportion of cause‐specific events overestimates the true quantity. In this paper, we show that the quantile residual life function, a natural and popular summary measure of survival data, could be also seriously affected by the competing events. An existing two‐sample test statistic for inference on median residual life is modified for competing risks data, which does not involve estimation of the improper probability density function of the subdistribution of cause‐specific events under censoring. Simulation results demonstrate that the test statistic controls the type 1 error probabilities reasonably well. 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Jeong and J. P. Fine</title><title type="short">Cause‐specific quantile residual life</title></titleGroup><creators><creator xml:id="bimj1366-cr-0001" creatorRole="author" affiliationRef="#bimj1366-aff-0001" corresponding="yes"><personName><givenNames>Jong‐Hyeon</givenNames><familyName>Jeong</familyName></personName></creator><creator xml:id="bimj1366-cr-0002" creatorRole="author" affiliationRef="#bimj1366-aff-0002"><personName><givenNames>Jason P.</givenNames><familyName>Fine</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="bimj1366-aff-0001" countryCode="US"><orgDiv>Department of Biostatistics</orgDiv><orgName>University of Pittsburgh</orgName><address><city>Pittsburgh</city><countryPart>PA</countryPart><postCode>15261</postCode><country>USA</country></address></affiliation><affiliation xml:id="bimj1366-aff-0002" countryCode="US"><orgDiv>Department of Biostatistics</orgDiv><orgName>University of North Carolina</orgName><address><city>Chapel Hill</city><countryPart>NC</countryPart><postCode>27599</postCode><country>USA</country></address></affiliation></affiliationGroup><keywordGroup type="author"><keyword xml:id="bimj1366-kwd-0001">Breast cancer</keyword><keyword xml:id="bimj1366-kwd-0002">Competing risks</keyword><keyword xml:id="bimj1366-kwd-0003">Minimum dispersion statistic</keyword><keyword xml:id="bimj1366-kwd-0004">Percentile residual life</keyword><keyword xml:id="bimj1366-kwd-0005">Survival data</keyword></keywordGroup><fundingInfo><fundingAgency>National Health Institute (NIH)</fundingAgency><fundingNumber>5‐U10‐CA69974‐09</fundingNumber><fundingNumber>5‐U10‐CA69651‐11</fundingNumber></fundingInfo><abstractGroup><abstract type="main"><p>In medical research, investigators are often interested in inferring time‐to‐event distributions under competing risks. It is well known, however, that the naive approach based on the Kaplan–Meier method to estimate the proportion of cause‐specific events overestimates the true quantity. In this paper, we show that the quantile residual life function, a natural and popular summary measure of survival data, could be also seriously affected by the competing events. An existing two‐sample test statistic for inference on median residual life is modified for competing risks data, which does not involve estimation of the improper probability density function of the subdistribution of cause‐specific events under censoring. Simulation results demonstrate that the test statistic controls the type 1 error probabilities reasonably well. The proposed method is applied to a real data example from a large‐scale phase III breast cancer study.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Nonparametric inference on cause‐specific quantile residual life</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Cause‐specific quantile residual life</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Nonparametric inference on cause‐specific quantile residual life</title></titleInfo><name type="personal"><namePart type="given">Jong‐Hyeon</namePart><namePart type="family">Jeong</namePart><affiliation>Department of Biostatistics, University of Pittsburgh, PA, 15261, Pittsburgh, USA</affiliation><affiliation>E-mail: jeong@nsabp.pitt.edu</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Jason P.</namePart><namePart type="family">Fine</namePart><affiliation>Department of Biostatistics, University of North Carolina, NC, 27599, Chapel Hill, USA</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article" authority="ISTEX" authorityURI="https://content-type.data.istex.fr" valueURI="https://content-type.data.istex.fr/ark:/67375/XTP-6N5SZHKN-D">article</genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><dateIssued encoding="w3cdtf">2013-01</dateIssued><dateCreated encoding="w3cdtf">2012-09-06</dateCreated><dateCaptured encoding="w3cdtf">2011-09-09</dateCaptured><dateValid encoding="w3cdtf">2012-08-20</dateValid><copyrightDate encoding="w3cdtf">2013</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><abstract>In medical research, investigators are often interested in inferring time‐to‐event distributions under competing risks. It is well known, however, that the naive approach based on the Kaplan–Meier method to estimate the proportion of cause‐specific events overestimates the true quantity. In this paper, we show that the quantile residual life function, a natural and popular summary measure of survival data, could be also seriously affected by the competing events. An existing two‐sample test statistic for inference on median residual life is modified for competing risks data, which does not involve estimation of the improper probability density function of the subdistribution of cause‐specific events under censoring. Simulation results demonstrate that the test statistic controls the type 1 error probabilities reasonably well. The proposed method is applied to a real data example from a large‐scale phase III breast cancer study.</abstract><note type="funding">National Health Institute (NIH) - No. 5‐U10‐CA69974‐09; No. 5‐U10‐CA69651‐11; </note><subject><genre>keywords</genre><topic>Breast cancer</topic><topic>Competing risks</topic><topic>Minimum dispersion statistic</topic><topic>Percentile residual life</topic><topic>Survival data</topic></subject><relatedItem type="host"><titleInfo><title>Biometrical Journal</title></titleInfo><titleInfo type="abbreviated"><title>Biom. J.</title></titleInfo><genre type="journal" authority="ISTEX" authorityURI="https://publication-type.data.istex.fr" valueURI="https://publication-type.data.istex.fr/ark:/67375/JMC-0GLKJH51-B">journal</genre><subject><genre>article-category</genre><topic>Research Paper</topic></subject><identifier type="ISSN">0323-3847</identifier><identifier type="eISSN">1521-4036</identifier><identifier type="DOI">10.1002/(ISSN)1521-4036</identifier><identifier type="PublisherID">BIMJ</identifier><part><date>2013</date><detail type="volume"><caption>vol.</caption><number>55</number></detail><detail type="issue"><caption>no.</caption><number>1</number></detail><extent unit="pages"><start>68</start><end>81</end><total>14</total></extent></part></relatedItem><identifier type="istex">A78BF5520AAF2DA7133DDCB263D72F956CBC011B</identifier><identifier type="ark">ark:/67375/WNG-G4MPMZ8Z-N</identifier><identifier type="DOI">10.1002/bimj.201100190</identifier><identifier type="ArticleID">BIMJ1366</identifier><accessCondition type="use and reproduction" contentType="copyright">© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim</accessCondition><recordInfo><recordContentSource authority="ISTEX" authorityURI="https://loaded-corpus.data.istex.fr" valueURI="https://loaded-corpus.data.istex.fr/ark:/67375/XBH-L0C46X92-X">wiley</recordContentSource></recordInfo></mods><json:item><extension>json</extension><original>false</original><mimetype>application/json</mimetype><uri>https://api.istex.fr/document/A78BF5520AAF2DA7133DDCB263D72F956CBC011B/metadata/json</uri></json:item></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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