Statistical Methods and Computing for Semiparametric Accelerated Failure Time Model with Induced Smoothing
Digital Document
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Handle
http://hdl.handle.net/11134/20002:860634151
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Persons
Author (aut): Chiou, Sy Han
Thesis advisor (ths): Yan, Jun
Associate Advisor (asa): Holsinger, Kent
Associate Advisor (asa): Kang, Sangwook
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Title |
Title
Title
Statistical Methods and Computing for Semiparametric Accelerated Failure Time Model with Induced Smoothing
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Origin Information
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Parent Item
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Resource Type
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Digital Origin |
Digital Origin
born digital
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Description |
Description
In survival analysis, semiparametric accelerated failure time (AFT) models directly relate the predicted failure times to covariates and are a useful alternative to relative risk models. Recent developments in rank-based estimation and least squares estimation provide promising tools to make the AFT models more attractive in practice. In this dissertation, we propose fast and accurate inferences for AFT models with applications under various sampling schemes. The challenge in computing the rank-based estimator comes from solving nonsmooth estimating equations. This difficulty can be overcome with an induced smoothing approach. We generalize the induced smoothing approach to incorporate weights with missing data arising from case-cohort study and stratified sampling design. Parameters are estimated with smoothed estimating equations. Variance estimators are obtained through efficient resampling methods that avoid full blown bootstrap. The estimator from the smooth weighted estimating equations are shown to be consistent and have the same asymptotic distribution as that from the nonsmooth version. An univariate failure time data from a tumor study and a clustered data from a dental study are analyzed. The induced smoothing approach for rank-based AFT models is natural with Gehan's weight. Using the estimator from induced smoothing with Gehan's weight as an initial value, we propose an iterative procedure that works for any weight of general form. The resulting estimator has the same asymptotic properties as the nonsmooth rank-based estimator with the same weight. Real data from an adolescent stress duration study and a case-cohort study for Wilm's tumor illustrate the methods. As for the least square estimation, we propose a generalized estimating equations (GEE) approach. The consistency of the regression coefficient estimator is robust to misspecification of working covariance and the efficiency is higher when the working covariance structure is closer to the truth. The marginal error distributions and regression coefficient are allowed be unique for each margin or partially shared across margins as needed. The resulting estimator is consistent and asymptotically normal, with variance estimated through a multiplier resampling method. Bivariate failure times data from a diabetic retinopathy study is analyzed. All the aforementioned methods for AFT models are implemented in an R package aftgee (http://cran.r-project.org/web/packages/aftgee/index.html).
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Genre
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Organizations
Degree granting institution (dgg): University of Connecticut
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Rights Statement |
Rights Statement
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Use and Reproduction |
Use and Reproduction
These Materials are provided for educational and research purposes only. The University of Connecticut Library holds the copyright except where noted. Permission must be obtained in writing from the University of Connecticut Library and/or the owner(s) of the copyright to publish reproductions or quotations beyond "fair use."
These materials are provided for educational and research purposes only.
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Degree Name |
Degree Name
Doctor of Philosophy
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Degree Level |
Degree Level
Doctoral
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Degree Discipline |
Degree Discipline
Statistics
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Local Identifier |
Local Identifier
ASC Theses 18395
841601777
OC_d_56
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