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Mixtures of polya trees for flexible...

Zhao, Luping.

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Mixtures of polya trees for flexible spatial survival modeling.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Mixtures of polya trees for flexible spatial survival modeling./
Author:	Zhao, Luping.
Description:	108 p.
Notes:	Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0036.
Contained By:	Dissertation Abstracts International69-01B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3295704
ISBN:	9780549405504

Mixtures of polya trees for flexible spatial survival modeling.
Zhao, Luping.

Mixtures of polya trees for flexible spatial survival modeling. - 108 p.

Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0036.

Thesis (Ph.D.)--University of Minnesota, 2008.

With the proliferation of spatially oriented time-to-event data, spatial modeling has received dramatically increased attention. The traditional way to capture a spatial pattern is to introduce frailty terms in the linear predictor. We introduce a flexible nonparametric mixture of Polya trees (MPT) prior to the spatial frailty models within three competing survival settings -- proportional hazards (PH), accelerated failure time (AFT), and proportional odds (PO). We then extend our working structure from spatially oriented time-to-event data to both spatially and temporally indexed time-to-event data Besides the spatial pattern, temporal cohort effects are also an interest of analyses for subjects who were diagnosed with the disease of interest (and thus, entered the study) during different time periods, e.g. calendar year. We develop semiparametric hierarchical Bayesian frailty models that conditionally follow a PH assumption to capture both spatial and temporal associations. A mixture of dependent Polya trees prior is developed as a flexible nonparametric approach. The dependency structure explicitly models evolution in baseline survival under a conditionally PH assumption. We also propose a new methodology to capture the spatial pattern other than the traditional spatial frailty method. The proposed PH model assumes a mixture of spatially dependent Polya trees prior based on Markov random fields for the baselines. Specifically, the logit transformed MPT conditional probabilities follow a proper conditional autoregressive (CAR) prior at each pair of companion sets in the partition defining the tailfree process. Thanks to modern Markov chain Monte Carlo (MCMC) methods; the proposed approaches remain computationally feasible in a fully hierarchical Bayesian framework. We illustrate the usefulness of our proposed methods with analyses of three spatially oriented breast cancer survival data from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. Our results indicate appreciable advantages for the proposed approaches over traditional alternatives according to Log pseudo marginal likelihood (LPML), deviance information criterion (DIC), and full sample score (FSS) statistics.

ISBN: 9780549405504Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Mixtures of polya trees for flexible spatial survival modeling.
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100 1 $a Zhao, Luping. $3 1263113
245 1 0 $a Mixtures of polya trees for flexible spatial survival modeling.
300 $a 108 p.
500 $a Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0036.
502 $a Thesis (Ph.D.)--University of Minnesota, 2008.
520 $a With the proliferation of spatially oriented time-to-event data, spatial modeling has received dramatically increased attention. The traditional way to capture a spatial pattern is to introduce frailty terms in the linear predictor. We introduce a flexible nonparametric mixture of Polya trees (MPT) prior to the spatial frailty models within three competing survival settings -- proportional hazards (PH), accelerated failure time (AFT), and proportional odds (PO). We then extend our working structure from spatially oriented time-to-event data to both spatially and temporally indexed time-to-event data Besides the spatial pattern, temporal cohort effects are also an interest of analyses for subjects who were diagnosed with the disease of interest (and thus, entered the study) during different time periods, e.g. calendar year. We develop semiparametric hierarchical Bayesian frailty models that conditionally follow a PH assumption to capture both spatial and temporal associations. A mixture of dependent Polya trees prior is developed as a flexible nonparametric approach. The dependency structure explicitly models evolution in baseline survival under a conditionally PH assumption. We also propose a new methodology to capture the spatial pattern other than the traditional spatial frailty method. The proposed PH model assumes a mixture of spatially dependent Polya trees prior based on Markov random fields for the baselines. Specifically, the logit transformed MPT conditional probabilities follow a proper conditional autoregressive (CAR) prior at each pair of companion sets in the partition defining the tailfree process. Thanks to modern Markov chain Monte Carlo (MCMC) methods; the proposed approaches remain computationally feasible in a fully hierarchical Bayesian framework. We illustrate the usefulness of our proposed methods with analyses of three spatially oriented breast cancer survival data from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute. Our results indicate appreciable advantages for the proposed approaches over traditional alternatives according to Log pseudo marginal likelihood (LPML), deviance information criterion (DIC), and full sample score (FSS) statistics.
590 $a School code: 0130.
650 4 $a Biology, Biostatistics. $3 1018416
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710 2 $a University of Minnesota. $3 676231
773 0 $t Dissertation Abstracts International $g 69-01B.
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