Semiparametric estimation of the accelerated failure time model with partly interval-censored data.

TitleSemiparametric estimation of the accelerated failure time model with partly interval-censored data.
Publication TypeJournal Article
Year of Publication2017
AuthorsGao, Fei, Donglin Zeng, and Dan-Yu Lin
JournalBiometrics
Volume73
Issue4
Pagination1161-1168
Date Published2017 12
ISSN1541-0420
KeywordsAcquired Immunodeficiency Syndrome, Biometry, Computer Simulation, Humans, Models, Statistical, Time Factors
Abstract

Partly interval-censored (PIC) data arise when some failure times are exactly observed while others are only known to lie within certain intervals. In this article, we consider efficient semiparametric estimation of the accelerated failure time (AFT) model with PIC data. We first generalize the Buckley-James estimator for right-censored data to PIC data. Then, we develop a one-step estimator by deriving and estimating the efficient score for the regression parameters. We show that under mild regularity conditions the generalized Buckley-James estimator is consistent and asymptotically normal and the one-step estimator is consistent and asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound. We conduct extensive simulation studies to examine the performance of the proposed estimators in finite samples and apply our methods to data derived from an AIDS study.

DOI10.1111/biom.12700
Alternate JournalBiometrics
Original PublicationSemiparametric estimation of the accelerated failure time model with partly interval-censored data.
PubMed ID28444688
PubMed Central IDPMC5785785
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R01 GM047845 / GM / NIGMS NIH HHS / United States