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Maximum likelihood estimation for semiparametric transformation models with interval-censored data
- Source :
- Biometrika
- Publication Year :
- 2016
- Publisher :
- Oxford University Press (OUP), 2016.
-
Abstract
- Interval censoring arises frequently in clinical, epidemiological, financial, and sociological studies, where the event or failure of interest is known only to occur within an interval induced by periodic monitoring. We formulate the effects of potentially time-dependent covariates on the interval-censored failure time through a broad class of semiparametric transformation models that encompasses proportional hazards and proportional odds models. We consider nonparametric maximum likelihood estimation for this class of models with an arbitrary number of monitoring times for each subject. We devise an EM-type algorithm that converges stably, even in the presence of time-dependent covariates, and show that the estimators for the regression parameters are consistent, asymptotically normal, and asymptotically efficient with an easily estimated covariance matrix. Finally, we demonstrate the performance of our procedures through extensive simulation studies and application to an HIV/AIDS study conducted in Thailand.<br />Comment: This paper has been withdrawn by the author due to some errors in the proofs
- Subjects :
- FOS: Computer and information sciences
Statistics and Probability
Interval censoring
Semiparametric efficiency
General Mathematics
Proportional hazards
Time-dependent covariate
Nonparametric likelihood
01 natural sciences
Methodology (stat.ME)
010104 statistics & probability
03 medical and health sciences
0302 clinical medicine
Statistics
Expectation–maximization algorithm
Covariate
Econometrics
Statistics::Methodology
030212 general & internal medicine
Semiparametric regression
0101 mathematics
EM algorithm
Statistics - Methodology
Mathematics
Proportional hazards model
Covariance matrix
Applied Mathematics
Estimator
Articles
Proportional odds
Current-status data
Agricultural and Biological Sciences (miscellaneous)
Censoring (statistics)
Semiparametric model
Statistics, Probability and Uncertainty
General Agricultural and Biological Sciences
Linear transformation model
Subjects
Details
- ISSN :
- 14643510 and 00063444
- Volume :
- 103
- Database :
- OpenAIRE
- Journal :
- Biometrika
- Accession number :
- edsair.doi.dedup.....c51e0a267934bd617c38993b2f46882d