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...

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Published in:	Biometrics Vol. 73; no. 3; pp. 857 - 865
Main Authors:	Mao, Lu, Lin, Dan-Yu, Zeng, Donglin
Format:	Journal Article
Language:	English
Published:	England Wiley-Blackwell 01.09.2017 Blackwell Publishing Ltd
Subjects:	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
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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
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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
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Keywords	Cumulative incidence Interval censoring Time-varying covariates Nonparametric maximum likelihood estimation Self-consistency algorithm Transformation models
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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...
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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
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