LEADER 00000nam  2200277   4500 
001    AAI3295704 
005    20081103100022.5 
008    081103s2008    ||||||||||||||||| ||eng d 
020    9780549405504 
035    (UMI)AAI3295704 
040    UMI|cUMI 
100 1  Zhao, Luping 
245 10 Mixtures of polya trees for flexible spatial survival 
       modeling 
300    108 p 
500    Source: Dissertation Abstracts International, Volume: 69-
       01, Section: B, page: 0036 
502    Thesis (Ph.D.)--University of Minnesota, 2008 
520    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    School code: 0130 
590    DDC 
650  4 Biology, Biostatistics 
690    0308 
710 2  University of Minnesota 
773 0  |tDissertation Abstracts International|g69-01B 
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/
       advanced?query=3295704