Semiparametric Regression Analysis of Interval-Censored Competing Risks Data

Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (exte...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	Biometrics Ročník 73; číslo 3; s. 857 - 865
Hlavní autoři:	Mao, Lu, Lin, Dan-Yu, Zeng, Donglin
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	England Wiley-Blackwell 01.09.2017 Blackwell Publishing Ltd
Témata:	algorithms biometry Computer Simulation Cumulative incidence Data processing DISCUSSION PAPER Distribution functions Economic models Empirical analysis Failure Hazards HIV HIV infections Human immunodeficiency virus Human immunodeficiency virus 1 Interval censoring Likelihood Functions Maximum likelihood estimation Models, Statistical Nonparametric maximum likelihood estimation Normality Parameter estimation Regression Analysis Regression models Risk Self‐consistency algorithm Time‐varying covariates Transformation models Cumulative incidence Interval censoring Time-varying covariates Nonparametric maximum likelihood estimation Self-consistency algorithm Transformation models
ISSN:	0006-341X, 1541-0420, 1541-0420
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM-type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators for the regression parameters by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV-1 infection with different viral subtypes.
AbstractList	Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM-type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators for the regression parameters by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV-1 infection with different viral subtypes. Summary Interval‐censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time‐varying (external) covariates on the cumulative incidence or sub‐distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non‐proportional hazards structures for the sub‐distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM‐type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators for the regression parameters by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV‐1 infection with different viral subtypes. Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM-type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators for the regression parameters by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV-1 infection with different viral subtypes.Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM-type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators for the regression parameters by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV-1 infection with different viral subtypes. Summary Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not observed directly but rather is known to lie in an interval between two examinations. We formulate the effects of possibly time-varying (external) covariates on the cumulative incidence or sub-distribution function of competing risks (i.e., the marginal probability of failure from a specific cause) through a broad class of semiparametric regression models that captures both proportional and non-proportional hazards structures for the sub-distribution. We allow each subject to have an arbitrary number of examinations and accommodate missing information on the cause of failure. We consider nonparametric maximum likelihood estimation and devise a fast and stable EM-type algorithm for its computation. We then establish the consistency, asymptotic normality, and semiparametric efficiency of the resulting estimators for the regression parameters by appealing to modern empirical process theory. In addition, we show through extensive simulation studies that the proposed methods perform well in realistic situations. Finally, we provide an application to a study on HIV-1 infection with different viral subtypes.
Author	Zeng, Donglin Mao, Lu Lin, Dan-Yu
AuthorAffiliation	2 Department of Biostatistics, CB 7420, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A 1 Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53792, U.S.A
AuthorAffiliation_xml	– name: 1 Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53792, U.S.A – name: 2 Department of Biostatistics, CB 7420, University of North Carolina, Chapel Hill, North Carolina 27599-7420, U.S.A
Author_xml	– sequence: 1 givenname: Lu surname: Mao fullname: Mao, Lu – sequence: 2 givenname: Dan-Yu surname: Lin fullname: Lin, Dan-Yu – sequence: 3 givenname: Donglin surname: Zeng fullname: Zeng, Donglin
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/28211951$$D View this record in MEDLINE/PubMed
BookMark	eNqFkdFrFDEQxoO02Ovpi-_Kgi9S2Jpkk-zmpVBPaw9OClXBt5DNzp45d5MzyVXuvzf1ekWLaF6GML_vY2a-Y3TgvAOEnhF8SvJ73Vo_nhIqBHuEJoQzUmJG8QGaYIxFWTHy5Qgdx7jKX8kxfYyOaEMJkZxM0OIjjHatgx4hBWuKa1gGiNF6V5w7PWyjjYXvi7lLEG70UM7ARR-gK2Z-XEOybllc2_gtFm910k_QYa-HCE_v6hR9vnj3aXZZLq7ez2fni9JwXLGyNrw2Gpq-63nP6l43VEjW1abtGQWCRUeBd41pNWhJ27rFXUVZK3VWyZ531RSd7XzXm3aEzoBLQQ9qHeyow1Z5bdWfHWe_qqW_UZwLwiuSDV7dGQT_fQMxqdFGA8OgHfhNVDSfijNR1_K_KGmElEJy3mT05QN05TchXzFTklWiIiLvP0Uvfh_-fup9JhnAO8AEH2OAXhmbdMqR5F3soAhWt7Gr29jVr9iz5OSBZO_6V5js4B92gO0_SPVmfvVhr3m-06xi8uFew5iQOTxa_QSGlMgu
CitedBy_id	crossref_primary_10_1080_02331888_2020_1811281 crossref_primary_10_1111_sjos_12768 crossref_primary_10_1080_02664763_2023_2230533 crossref_primary_10_29220_CSAM_2025_32_2_249 crossref_primary_10_1080_19466315_2020_1741445 crossref_primary_10_1002_sim_9927 crossref_primary_10_1002_sim_8201 crossref_primary_10_1111_biom_12970 crossref_primary_10_1080_03610918_2025_2499059 crossref_primary_10_1002_sim_9668 crossref_primary_10_1002_sim_8544 crossref_primary_10_1093_biostatistics_kxaa052 crossref_primary_10_1177_0962280220939123
Cites_doi	10.1111/j.2517-6161.1976.tb01597.x 10.1016/j.jmva.2015.10.001 10.1002/9781118032985 10.1093/biomet/82.4.835 10.1214/aos/1176350951 10.1111/biom.12389 10.1093/biomet/90.1.183 10.1017/CBO9781139020893 10.1093/biomet/89.3.659 10.1007/978-1-4684-6316-3_8 10.1093/biomet/91.2.331 10.1007/978-1-4757-2545-2 10.1080/01621459.1999.10474144 10.1111/biom.12109 10.1093/biomet/asw013 10.1214/aos/1032894452 10.1080/01621459.2000.10474219 10.1111/j.0006-341X.2001.00074.x 10.1093/biomet/ass053
ContentType	Journal Article
Copyright	Copyright © 2017 International Biometric Society 2017, The International Biometric Society 2017, The International Biometric Society.
Copyright_xml	– notice: Copyright © 2017 International Biometric Society – notice: 2017, The International Biometric Society – notice: 2017, The International Biometric Society.
DBID	AAYXX CITATION CGR CUY CVF ECM EIF NPM JQ2 7X8 7S9 L.6 5PM
DOI	10.1111/biom.12664
DatabaseName	CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Computer Science Collection MEDLINE - Academic AGRICOLA AGRICOLA - Academic PubMed Central (Full Participant titles)
DatabaseTitle	CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) ProQuest Computer Science Collection MEDLINE - Academic AGRICOLA AGRICOLA - Academic
DatabaseTitleList	MEDLINE AGRICOLA CrossRef MEDLINE - Academic ProQuest Computer Science Collection
Database_xml	– sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Statistics Biology Mathematics
EISSN	1541-0420
EndPage	865
ExternalDocumentID	PMC5561531 28211951 10_1111_biom_12664 BIOM12664 44698262
Genre	article Journal Article
GrantInformation_xml	– fundername: NCI NIH HHS grantid: P01 CA142538 – fundername: NIGMS NIH HHS grantid: R01 GM047845 – fundername: NIAID NIH HHS grantid: R37 AI029168 – fundername: NIAID NIH HHS grantid: R01 AI029168 – fundername: NIAID NIH HHS grantid: P30 AI050410 – fundername: NCI NIH HHS grantid: R01 CA082659
GroupedDBID	--- -~X .3N .4S .DC .GA 05W 0R~ 10A 1OC 23N 2AX 2QV 33P 36B 3SF 4.4 44B 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 53G 5GY 5HH 5LA 5RE 5VS 66C 6J9 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 A8Z AAESR AAEVG AAHBH AAMMB AANLZ AAONW AASGY AAUAY AAWIL AAXRX AAYCA AAZKR AAZSN ABAWQ ABBHK ABCQN ABCUV ABDBF ABDFA ABEJV ABEML ABFAN ABGNP ABJNI ABLJU ABMNT ABPPZ ABPVW ABXSQ ABXVV ABYWD ACAHQ ACCZN ACFBH ACGFO ACGFS ACGOD ACHJO ACIWK ACMTB ACNCT ACPOU ACPRK ACSCC ACTMH ACUHS ACXBN ACXQS ADBBV ADEOM ADIPN ADIZJ ADKYN ADMGS ADNBA ADODI ADOZA ADVOB ADXAS ADZMN AEFGJ AEGXH AEIGN AEIMD AENEX AEOTA AEUPB AEUYR AFBPY AFDVO AFEBI AFGKR AFVYC AFWVQ AFZJQ AGLNM AGORE AGTJU AGXDD AHMBA AIAGR AIDQK AIDYY AIHAF AIURR AJAOE AJNCP AJXKR ALAGY ALIPV ALMA_UNASSIGNED_HOLDINGS ALRMG ALUQN AMBMR AMYDB APXXL ARCSS ATUGU AUFTA AZBYB AZVAB BAFTC BCRHZ BDRZF BENPR BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CS3 D-E D-F DCZOG DPXWK DQDLB DR2 DRFUL DRSTM DSRWC DXH EAD EAP EBC EBD EBS ECEWR EDO EJD EMB EMK EMOBN EST ESX F00 F01 F04 F5P FD6 G-S G.N GODZA GS5 H.T H.X HQ6 HZI HZ~ IPSME IX1 J0M JAAYA JAC JBMMH JBZCM JENOY JHFFW JKQEH JLEZI JLXEF JMS JPL JST K48 KOP LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES MK4 MRFUL MRSTM MSFUL MSSTM MVM MXFUL MXSTM N04 N05 N9A NF~ NU- O66 O9- OIG OJZSN OWPYF P2P P2W P2X P4D PQQKQ Q.N Q11 QB0 R.K ROL ROX RX1 RXW SA0 SUPJJ SV3 TN5 TUS UB1 V8K W8V W99 WBKPD WH7 WIH WIK WOHZO WQJ WYISQ X6Y XBAML XG1 XSW ZZTAW ~02 ~IA ~KM ~WT .GJ .Y3 3-9 31~ 7X7 88E 88I 8AF 8C1 8FE 8FG 8FH 8FI 8FJ 8R4 8R5 AANHP ABJCF ABUWG ACBWZ ACKIV ACRPL ACYXJ ADNMO ADULT AFKRA AGQPQ AHGBF AJBYB ALEEW ARAPS ASPBG AS~ AVWKF AZFZN AZQEC BBNVY BGLVJ BHPHI BPHCQ BVXVI CAG CCPQU COF DWQXO ESTFP FEDTE FXEWX FYUFA GNUQQ H13 HCIFZ HF~ HGD HMCUK HVGLF IHE K6V K7- L6V LK8 M1P M2P M7P M7S NHB P0- P62 PHGZM PHGZT PJZUB PPXIY PQGLB PROAC PSQYO PTHSS PUEGO Q2X RNS RWL TAE UAP UKHRP ZGI ZXP ZY4 AAYXX CITATION O8X CGR CUY CVF ECM EIF NPM JQ2 7X8 7S9 L.6 5PM
ID	FETCH-LOGICAL-c5034-7c57cae8fdf5f47fa82694d7cbf42e106d2e5d8cbaea92b7b0d324b9a7ca9f5d3
IEDL.DBID	DRFUL
ISICitedReferencesCount	18
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000411878000017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	0006-341X 1541-0420
IngestDate	Tue Nov 04 01:58:21 EST 2025 Fri Oct 03 00:06:48 EDT 2025 Sun Nov 09 13:43:52 EST 2025 Wed Aug 13 04:41:11 EDT 2025 Mon Jul 21 05:52:01 EDT 2025 Tue Nov 18 22:46:19 EST 2025 Sat Nov 29 02:10:03 EST 2025 Sun Sep 21 06:15:59 EDT 2025 Thu Jul 03 22:05:53 EDT 2025
IsDoiOpenAccess	false
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	3
Keywords	Cumulative incidence Interval censoring Time-varying covariates Nonparametric maximum likelihood estimation Self-consistency algorithm Transformation models
Language	English
License	https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model 2017, The International Biometric Society.
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c5034-7c57cae8fdf5f47fa82694d7cbf42e106d2e5d8cbaea92b7b0d324b9a7ca9f5d3
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 dzeng@email.unc.edu lin@bios.unc.edu lmao@biostat.wisc.edu
OpenAccessLink	https://www.ncbi.nlm.nih.gov/pmc/articles/5561531
PMID	28211951
PQID	1943631650
PQPubID	35366
PageCount	9
ParticipantIDs	pubmedcentral_primary_oai_pubmedcentral_nih_gov_5561531 proquest_miscellaneous_2000546779 proquest_miscellaneous_1869969558 proquest_journals_1943631650 pubmed_primary_28211951 crossref_citationtrail_10_1111_biom_12664 crossref_primary_10_1111_biom_12664 wiley_primary_10_1111_biom_12664_BIOM12664 jstor_primary_44698262
PublicationCentury	2000
PublicationDate	September 2017
PublicationDateYYYYMMDD	2017-09-01
PublicationDate_xml	– month: 09 year: 2017 text: September 2017
PublicationDecade	2010
PublicationPlace	England
PublicationPlace_xml	– name: England – name: Washington
PublicationTitle	Biometrics
PublicationTitleAlternate	Biometrics
PublicationYear	2017
Publisher	Wiley-Blackwell Blackwell Publishing Ltd
Publisher_xml	– name: Wiley-Blackwell – name: Blackwell Publishing Ltd
References	2008a; 36 1995; 82 2014; 70 2003; 90 2015; 72 2002; 89 1988; 16 1997 2013; 100 2000; 95 1996 2016; 143 2006 2008b; 36 2016 1993 2014 1999; 94 2002 2016; 103 1996; 24 2004; 91 2001; 57 1976; 38 Li (2024011603223859700_biom12664-bib-0016) 2013; 100 Cheng (2024011603223859700_biom12664-bib-0003) 1995; 82 Groeneboom (2024011603223859700_biom12664-bib-0006) 2014 Zeng (2024011603223859700_biom12664-bib-0024) 2016; 103 Lu (2024011603223859700_biom12664-bib-0017) 2004; 91 Jewell (2024011603223859700_biom12664-bib-0013) 2003; 90 Gray (2024011603223859700_biom12664-bib-0005) 1988; 16 Turnbull (2024011603223859700_biom12664-bib-0021) 1976; 38 Hudgens (2024011603223859700_biom12664-bib-0012) 2014; 70 Mao (2024011603223859700_biom12664-bib-0018) 2016 Martinussen (2024011603223859700_biom12664-bib-0019) 2006 Wang (2024011603223859700_biom12664-bib-0023) 2015; 72 Hudgens (2024011603223859700_biom12664-bib-0011) 2001; 57 Huang (2024011603223859700_biom12664-bib-0010) 1997 Fine (2024011603223859700_biom12664-bib-0004) 1999; 94 Kalbfleisch (2024011603223859700_biom12664-bib-0014) 2002 Li (2024011603223859700_biom12664-bib-0015) 2016; 143 Bickel (2024011603223859700_biom12664-bib-0001) 1993 Groeneboom (2024011603223859700_biom12664-bib-0007) 2008; 36 van der Vaart (2024011603223859700_biom12664-bib-0022) 1996 Groeneboom (2024011603223859700_biom12664-bib-0008) 2008; 36 Chen (2024011603223859700_biom12664-bib-0002) 2002; 89 Huang (2024011603223859700_biom12664-bib-0009) 1996; 24 Murphy (2024011603223859700_biom12664-bib-0020) 2000; 95
References_xml	– volume: 94 start-page: 496 year: 1999 end-page: 509 article-title: A proportional hazards model for the subdistribution of a competing risk publication-title: Journal of the American Statistical Association – volume: 24 start-page: 540 year: 1996 end-page: 568 article-title: Efficient estimation for the proportional hazards model with interval censoring publication-title: The Annals of Statistics – volume: 36 start-page: 1031 year: 2008a end-page: 1063 article-title: Current status data with competing risks: Consistency and rates of convergence of the MLE publication-title: The Annals of Statistics – volume: 38 start-page: 290 year: 1976 end-page: 295 article-title: The empirical distribution function with arbitrarily grouped, censored and truncated data publication-title: Journal of the Royal Statistical Society, Series B – volume: 89 start-page: 659 year: 2002 end-page: 668 article-title: Semiparametric analysis of transformation models with censored data publication-title: Biometrika – year: 2002 – year: 2016 article-title: Efficient estimation of semiparametric transformation models for the cumulative incidence of competing risks publication-title: Journal of the Royal Statistical Society – volume: 95 start-page: 449 year: 2000 end-page: 465 article-title: On profile likelihood publication-title: Journal of the American Statistical Association – volume: 36 start-page: 1064 year: 2008b end-page: 1089 article-title: Current status data with competing risks: Limiting distribution of the MLE publication-title: The Annals of Statistics – start-page: 123 year: 1997 end-page: 169 – year: 2006 – volume: 90 start-page: 183 year: 2003 end-page: 197 article-title: Nonparametric estimation from current status data with competing risks publication-title: Biometrika – volume: 143 start-page: 327 year: 2016 end-page: 344 article-title: The Fine‐Gray model under interval censored competing risks data publication-title: Journal of Multivariate Analysis – volume: 100 start-page: 173 year: 2013 end-page: 187 article-title: Smoothed nonparametric estimation for current status competing risks data publication-title: Biometrika – year: 1996 – volume: 82 start-page: 835 year: 1995 end-page: 845 article-title: Analysis of transformation models with censored data publication-title: Biometrika – volume: 72 start-page: 222 year: 2015 end-page: 231 article-title: A flexible, computationally efficient method for fitting the proportional hazards model to interval censored data publication-title: Biometrics – volume: 16 start-page: 1141 year: 1988 end-page: 1154 article-title: A class of K‐sample tests for comparing the cumulative incidence of a competing risk publication-title: The Annals of Statistics – volume: 70 start-page: 1 year: 2014 end-page: 9 article-title: Parametric likelihood inference for interval censored competing risks data publication-title: Biometrics – year: 1993 – volume: 103 start-page: 253 year: 2016 end-page: 271 article-title: Maximum likelihood estimation for semiparametric transformation models with interval‐censored data publication-title: Biometrika – year: 2014 – volume: 57 start-page: 74 year: 2001 end-page: 80 article-title: Nonparametric maximum likelihood estimation for competing risks survival data subject to interval censoring and truncation publication-title: Biometrics – volume: 91 start-page: 331 year: 2004 end-page: 343 article-title: On semiparametric transformation cure models publication-title: Biometrika – volume: 38 start-page: 290 year: 1976 ident: 2024011603223859700_biom12664-bib-0021 article-title: The empirical distribution function with arbitrarily grouped, censored and truncated data publication-title: Journal of the Royal Statistical Society, Series B doi: 10.1111/j.2517-6161.1976.tb01597.x – volume-title: Efficient and Adaptive Estimation for Semiparametric Models year: 1993 ident: 2024011603223859700_biom12664-bib-0001 – volume: 36 start-page: 1064 year: 2008 ident: 2024011603223859700_biom12664-bib-0008 article-title: Current status data with competing risks: Limiting distribution of the MLE publication-title: The Annals of Statistics – volume: 143 start-page: 327 year: 2016 ident: 2024011603223859700_biom12664-bib-0015 article-title: The Fine-Gray model under interval censored competing risks data publication-title: Journal of Multivariate Analysis doi: 10.1016/j.jmva.2015.10.001 – volume-title: The Statistical Analysis of Failure Time Data year: 2002 ident: 2024011603223859700_biom12664-bib-0014 doi: 10.1002/9781118032985 – volume: 82 start-page: 835 year: 1995 ident: 2024011603223859700_biom12664-bib-0003 article-title: Analysis of transformation models with censored data publication-title: Biometrika doi: 10.1093/biomet/82.4.835 – volume: 16 start-page: 1141 year: 1988 ident: 2024011603223859700_biom12664-bib-0005 article-title: A class of K-sample tests for comparing the cumulative incidence of a competing risk publication-title: The Annals of Statistics doi: 10.1214/aos/1176350951 – volume-title: Dynamic Regression Models for Survival Data year: 2006 ident: 2024011603223859700_biom12664-bib-0019 – volume: 72 start-page: 222 year: 2015 ident: 2024011603223859700_biom12664-bib-0023 article-title: A flexible, computationally efficient method for fitting the proportional hazards model to interval censored data publication-title: Biometrics doi: 10.1111/biom.12389 – year: 2016 ident: 2024011603223859700_biom12664-bib-0018 article-title: Efficient estimation of semiparametric transformation models for the cumulative incidence of competing risks publication-title: Journal of the Royal Statistical Society – volume: 90 start-page: 183 year: 2003 ident: 2024011603223859700_biom12664-bib-0013 article-title: Nonparametric estimation from current status data with competing risks publication-title: Biometrika doi: 10.1093/biomet/90.1.183 – volume-title: Nonparametric Estimation Under Shape Constraints: Estimators, Algorithms and Asymptotics year: 2014 ident: 2024011603223859700_biom12664-bib-0006 doi: 10.1017/CBO9781139020893 – volume: 89 start-page: 659 year: 2002 ident: 2024011603223859700_biom12664-bib-0002 article-title: Semiparametric analysis of transformation models with censored data publication-title: Biometrika doi: 10.1093/biomet/89.3.659 – start-page: 123 volume-title: Proceedings of the First Seattle Symposium in Biostatistics: Survival Analysis year: 1997 ident: 2024011603223859700_biom12664-bib-0010 article-title: Interval censored survival data: A review of recent progress% doi: 10.1007/978-1-4684-6316-3_8 – volume: 91 start-page: 331 year: 2004 ident: 2024011603223859700_biom12664-bib-0017 article-title: On semiparametric transformation cure models publication-title: Biometrika doi: 10.1093/biomet/91.2.331 – volume-title: Weak Convergence and Empirical Processes year: 1996 ident: 2024011603223859700_biom12664-bib-0022 doi: 10.1007/978-1-4757-2545-2 – volume: 94 start-page: 496 year: 1999 ident: 2024011603223859700_biom12664-bib-0004 article-title: A proportional hazards model for the subdistribution of a competing risk publication-title: Journal of the American Statistical Association doi: 10.1080/01621459.1999.10474144 – volume: 70 start-page: 1 year: 2014 ident: 2024011603223859700_biom12664-bib-0012 article-title: Parametric likelihood inference for interval censored competing risks data publication-title: Biometrics doi: 10.1111/biom.12109 – volume: 103 start-page: 253 year: 2016 ident: 2024011603223859700_biom12664-bib-0024 article-title: Maximum likelihood estimation for semiparametric transformation models with interval-censored data publication-title: Biometrika doi: 10.1093/biomet/asw013 – volume: 24 start-page: 540 year: 1996 ident: 2024011603223859700_biom12664-bib-0009 article-title: Efficient estimation for the proportional hazards model with interval censoring publication-title: The Annals of Statistics doi: 10.1214/aos/1032894452 – volume: 95 start-page: 449 year: 2000 ident: 2024011603223859700_biom12664-bib-0020 article-title: On profile likelihood publication-title: Journal of the American Statistical Association doi: 10.1080/01621459.2000.10474219 – volume: 36 start-page: 1031 year: 2008 ident: 2024011603223859700_biom12664-bib-0007 article-title: Current status data with competing risks: Consistency and rates of convergence of the MLE publication-title: The Annals of Statistics – volume: 57 start-page: 74 year: 2001 ident: 2024011603223859700_biom12664-bib-0011 article-title: Nonparametric maximum likelihood estimation for competing risks survival data subject to interval censoring and truncation publication-title: Biometrics doi: 10.1111/j.0006-341X.2001.00074.x – volume: 100 start-page: 173 year: 2013 ident: 2024011603223859700_biom12664-bib-0016 article-title: Smoothed nonparametric estimation for current status competing risks data publication-title: Biometrika doi: 10.1093/biomet/ass053
SSID	ssj0009502
Score	2.3112028
Snippet	Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not... Summary Interval‐censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time... Summary Interval-censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time... Interval‐censored competing risks data arise when each study subject may experience an event or failure from one of several causes and the failure time is not...
SourceID	pubmedcentral proquest pubmed crossref wiley jstor
SourceType	Open Access Repository Aggregation Database Index Database Enrichment Source Publisher
StartPage	857
SubjectTerms	algorithms biometry Computer Simulation Cumulative incidence Data processing DISCUSSION PAPER Distribution functions Economic models Empirical analysis Failure Hazards HIV HIV infections Human immunodeficiency virus Human immunodeficiency virus 1 Interval censoring Likelihood Functions Maximum likelihood estimation Models, Statistical Nonparametric maximum likelihood estimation Normality Parameter estimation Regression Analysis Regression models Risk Self‐consistency algorithm Time‐varying covariates Transformation models
Title	Semiparametric Regression Analysis of Interval-Censored Competing Risks Data
URI	https://www.jstor.org/stable/44698262 https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fbiom.12664 https://www.ncbi.nlm.nih.gov/pubmed/28211951 https://www.proquest.com/docview/1943631650 https://www.proquest.com/docview/1869969558 https://www.proquest.com/docview/2000546779 https://pubmed.ncbi.nlm.nih.gov/PMC5561531
Volume	73
WOSCitedRecordID	wos000411878000017&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVWIB databaseName: Wiley Online Library - Journals customDbUrl: eissn: 1541-0420 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009502 issn: 0006-341X databaseCode: DRFUL dateStart: 19990101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3bbtNAEB21KUjlgUugxVAqI3gBySjZ7HrXEi_cIpDagICivFl7hUitU8UUiTc-gW_kS5hZOyYRBQnxZsmzknd3xnPGPnsG4L7FqkGPjMlE7gwWKGaQGRtE5jDVYMIouI0f9D8cyMlETafFmw14vDwL0-hDdB_cKDLi-5oCXJt6JcjpePqjIeYXvglbDB2X92Dr-dvx0cGK6O6gUQsnfhcfTlt5UmLy_Bq9lpAaTuJ5aPN30uQqmI3ZaHzl_-ZxFS63KDR90rjNNdjwVR8uNn0pv_bh0mEn5lr3YZsAaaPnfB0m7_zJjPTCT6gVl00X_mPDpK1S3eqbpPOQziKVUh__-PYdZ1LPF96lNkJ0TJUp8dnrlMipN-Bo_OL9s5dZ25MhswKXOJNWSKu9Ci6IwGXQio7COmlN4MxjfemYF05Zo70umJFm4BCymULjqCIIN9qBXjWv_E1IVW4cZ5bjMgy4zbXiAfGJE4wHW2gmEniw3JjStoLl1DfjuFwWLrR0ZVy6BO51tqeNTMe5VjtxfzsTTv0zWc4S2FtueNnGcF2io47y0RAhbAJ3u9sYffRLRVd-foY2KseCsRBC_dmGRVycS1kksNv4UPcAWPCS5t4wAbnmXZ0BqX-v36lmn6IKOPU1xRdoAg-jd_1l2uXTV68P49WtfzG-DduMMEwk1O1B7_PizN-BC_YLutxiHzblVO23EfcTJugxBg
linkProvider	Wiley-Blackwell
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3bbtNAEB1BCqI8cAkUDAWM4AUkI2e967UfuUWtSAKCFuXN2itEah2UUCTe-AS-kS9hZu2YRBQkxJsljyXv7oznzPrsGYCHBqsGlWmdiNxqLFB0mmjjRWIx1WDCKLkJG_rvR3IyKabT8k3LzaGzMI0-RLfhRpERvtcU4LQhvRbldD79yQATDD8LWxz9SPRg68Xb4eFoTXU3beTCieDFB9NWn5SoPL-e3shIDSnxNLj5O2tyHc2GdDS8_J8DuQKXWhwaP20c5yqccXUfzjedKb_24eK4k3Nd9mGbIGmj6HwNJu_c8YwUw4-pGZeJF-5Dw6WtY9UqnMRzH88CmVId_fj2HYeynC-cjU0A6ZgsY2K0L2Oip16Hw-HLg-d7SduVITEizXgijZBGucJbLzyXXhV0GNZKoz1nDitMy5ywhdHKqZJpqVOLoE2XCp8qvbDZDvTqee1uQlzk2nJmOE5Dyk2uCu4RoVjBuDelYiKCR6uVqUwrWU6dM46qVelCU1eFqYvgQWf7qRHqONVqJyxwZ8KpgybLWQS7qxWv2iheVuiqWZ4NEMRGcL-7jfFHP1VU7eYnaFPkWDKWQhR_tmEBGedSlhHcaJyoewEseUl1bxCB3HCvzoD0vzfv1LOPQQecOpviJzSCx8G9_jLs6tn-63G4uvUvxvfgwt7BeFSN9ievbsM2I0QT6HW70Pu8OHF34Jz5gu63uNsG3k8cZzQO
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3bbtNAEB1BC1V54BJaMBRYBC8gGTnrXV8egRJRkYYKaJU3a68QqXWqhCLxxifwjXwJM2vHJKIgId4seVby7s54zthnzwA8Nlg1qFTrWGZWY4Gik1gbL2OLqQYTRilM-KB_NMxHo2I8Lg9abg6dhWn0IboPbhQZ4X1NAe5OrV-Kcjqf_qyPCUZchHUhywzjcn333eBwuKS6mzRy4UTwEv1xq09KVJ5fo1cyUkNKPA9u_s6aXEazIR0Nrv3nRK7D1RaHsueN49yAC67uweWmM-XXHlzZ7-Rc5z3YJEjaKDrfhNF7dzIhxfATasZl2Mx9bLi0NVOtwgmbejYJZEp1_OPbd5zKfDpzlpkA0jFZMmK0zxnRU7fgcPDqw8vXcduVITYySUWcG5kb5QpvvfQi96qgw7A2N9oL7rDCtNxJWxitnCq5znViEbTpUuGo0kubbsNaPa3dbWBFpq3gRuAyJMJkqhAeEYqVXHhTKi4jeLLYmcq0kuXUOeO4WpQutHRVWLoIHnW2p41Qx7lW22GDOxNBHTR5xiPYWex41UbxvEJXTbO0jyA2gofdbYw_-qmiajc9Q5siw5KxlLL4sw0PyDjL8zKCW40TdQ-AJS-p7vUjyFfcqzMg_e_VO_XkU9ABp86m-AqN4Glwr79Mu3qx93Y_XN35F-MHsHGwO6iGe6M3d2GTE6AJ7LodWPs8O3P34JL5gt43u9_G3U9eXTOJ
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Semiparametric+regression+analysis+of+interval-censored+competing+risks+data&rft.jtitle=Biometrics&rft.au=Mao%2C+Lu&rft.au=Lin%2C+Dan-Yu&rft.au=Zeng%2C+Donglin&rft.date=2017-09-01&rft.eissn=1541-0420&rft.volume=73&rft.issue=3&rft.spage=857&rft_id=info:doi/10.1111%2Fbiom.12664&rft_id=info%3Apmid%2F28211951&rft.externalDocID=28211951
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0006-341X&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0006-341X&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0006-341X&client=summon