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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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
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Shrnutí: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.
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dzeng@email.unc.edu
lin@bios.unc.edu
lmao@biostat.wisc.edu
ISSN:0006-341X
1541-0420
1541-0420
DOI:10.1111/biom.12664