Quantile regression for competing risks data with missing cause of failure
نویسندگان
چکیده
منابع مشابه
Quantile Regression for Competing Risks Data with Missing Cause of Failure.
This paper considers generalized linear quantile regression for competing risks data when the failure type may be missing. Two estimation procedures for the regression co-efficients, including an inverse probability weighted complete-case estimator and an augmented inverse probability weighted estimator, are discussed under the assumption that the failure type is missing at random. The proposed...
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This paper considers quantile regression analysis based on semi-competing risks data in which a non-terminal event may be dependently censored by a terminal event. The major interest is the covariate effects on the quantile of the non-terminal event time. Dependent censoring is handled by assuming that the joint distribution of the two event times follows a parametric copula model with unspecif...
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Competing risks data arise when study subjects may experience several different types of failure. It is common that the cause of failure is missing due to various reasons. Analysis of competing risks data with missing cause of failure has received considerable attention recently (Goetghebeur and Ryan (1995), Lu and Tsiatis (2001), Gao and Tsiatis (2005), among others). In this article, we study...
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In competing risks data, missing failure types (causes) is a very common phenomenon. In a general missing pattern, if a failure type is not observed, one observes a set of possible types containing the true type along with the failure time. Dewanji and Sengupta (2003) considered nonparametric estimation of the cause-specific hazard rates and suggested a Nelson-Aalen type estimator under such ge...
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ژورنال
عنوان ژورنال: Statistica Sinica
سال: 2012
ISSN: 1017-0405
DOI: 10.5705/ss.2010.093