Abstract/Details

Marginal quantile regression methods for censored multiple event times


2006 2006

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Abstract (summary)

In many clinical trials or epidemiologic studies it is not uncommon to observe multiple events or failures for the same study subject. Examples include the sequence of tumor recurrences, asthmatic attacks, epileptic seizures or infection episodes in an individual. Multiple event times provide additional valuable information, but, at the same time, may introduce more complicated issues into analysis. A major difficulty involved is to specify a proper dependence structure between duration times from the same individual.

This thesis develops statistical models and methods for the analysis of censored recurrent event times, where the censoring variables are usually always observable. Through marginal approach, which bypass the difficulty of specifying correct joint distribution of error, a censored quantile regression model that parallels that of Powell's (1984, 1986) censored quantile regression model in econometrics study is proposed for multiple event times with the accelerated failure time model in survival analysis. A modified convex loss function with small positive threshold is used for estimator estimation and covariance matrix estimation. The large sample properties and numerical study are provided for this method.

Indexing (details)


Subject
Statistics
Classification
0463: Statistics
Identifier / keyword
Pure sciences; Censored; Event times; Marginal quantile regression; Multiple-event times; Quantile regression
Title
Marginal quantile regression methods for censored multiple event times
Author
Zhang, Daqing
Number of pages
110
Publication year
2006
Degree date
2006
School code
0054
Source
DAI-B 66/12, Dissertation Abstracts International
Place of publication
Ann Arbor
Country of publication
United States
ISBN
9780542463846, 0542463849
Advisor
Ying, Zhiliang
University/institution
Columbia University
University location
United States -- New York
Degree
Ph.D.
Source type
Dissertations & Theses
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
3199598
ProQuest document ID
305345456
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
http://search.proquest.com/docview/305345456
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