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Bayesian joint modeling of longitudi...

Zhang, Qiang.

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Bayesian joint modeling of longitudinal and survival data.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bayesian joint modeling of longitudinal and survival data./
Author:	Zhang, Qiang.
Description:	68 p.
Notes:	Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 2904.
Contained By:	Dissertation Abstracts International66-06B.
Subject:	Biology, Biostatistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3178919
ISBN:	0542189127

Bayesian joint modeling of longitudinal and survival data.
Zhang, Qiang.

Bayesian joint modeling of longitudinal and survival data. - 68 p.

Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 2904.

Thesis (Ph.D.)--The University of Texas School of Public Health, 2005.

The joint modeling of longitudinal and survival data is a new approach to many applications such as HIV, cancer vaccine trials and quality of life studies. There are recent developments of the methodologies with respect to each of the components of the joint model as well as statistical processes that link them together. Among these, second order polynomial random effect models and linear mixed effects models are the most commonly used for the longitudinal trajectory function. In this study, we first relax the parametric constraints for polynomial random effect models by using Dirichlet process priors, then three longitudinal markers rather than only one marker are considered in one joint model. Second, we use a linear mixed effect model for the longitudinal process in a joint model analyzing the three markers. In this research these methods were applied to the Primary Biliary Cirrhosis sequential data, which were collected from a clinical trial of primary biliary cirrhosis (PBC) of the liver. This trial was conducted between 1974 and 1984 at the Mayo Clinic. The effects of three longitudinal markers (1) Total Serum Bilirubin, (2) Serum Albumin and (3) Serum Glutamic-Oxaloacetic transaminase (SGOT) on patients' survival were investigated. Proportion of treatment effect will also be studied using the proposed joint modeling approaches.

ISBN: 0542189127Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Bayesian joint modeling of longitudinal and survival data.
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100 1 $a Zhang, Qiang. $3 1906889
245 1 0 $a Bayesian joint modeling of longitudinal and survival data.
300 $a 68 p.
500 $a Source: Dissertation Abstracts International, Volume: 66-06, Section: B, page: 2904.
500 $a Supervisor: Peter Mueller.
502 $a Thesis (Ph.D.)--The University of Texas School of Public Health, 2005.
520 $a The joint modeling of longitudinal and survival data is a new approach to many applications such as HIV, cancer vaccine trials and quality of life studies. There are recent developments of the methodologies with respect to each of the components of the joint model as well as statistical processes that link them together. Among these, second order polynomial random effect models and linear mixed effects models are the most commonly used for the longitudinal trajectory function. In this study, we first relax the parametric constraints for polynomial random effect models by using Dirichlet process priors, then three longitudinal markers rather than only one marker are considered in one joint model. Second, we use a linear mixed effect model for the longitudinal process in a joint model analyzing the three markers. In this research these methods were applied to the Primary Biliary Cirrhosis sequential data, which were collected from a clinical trial of primary biliary cirrhosis (PBC) of the liver. This trial was conducted between 1974 and 1984 at the Mayo Clinic. The effects of three longitudinal markers (1) Total Serum Bilirubin, (2) Serum Albumin and (3) Serum Glutamic-Oxaloacetic transaminase (SGOT) on patients' survival were investigated. Proportion of treatment effect will also be studied using the proposed joint modeling approaches.
520 $a Based on the results, we conclude that the proposed modeling approaches yield better fit to the data and give less biased parameter estimates for these trajectory functions than previous methods. Model fit is also improved after considering three longitudinal markers instead of one marker only. The results from analysis of proportion of treatment effects from these joint models indicate same conclusion as that from the final model of Fleming and Harrington (1991), which is Bilirubin and Albumin together has stronger impact in predicting patients' survival and as a surrogate endpoints for treatment.
590 $a School code: 0219.
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Statistics. $3 517247
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710 2 0 $a The University of Texas School of Public Health. $3 1023950
773 0 $t Dissertation Abstracts International $g 66-06B.
790 1 0 $a Mueller, Peter, $e advisor
790 $a 0219
791 $a Ph.D.
792 $a 2005
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