Grant Details
Grant Number: |
5R03CA139538-02 Interpret this number |
Primary Investigator: |
Zhang, Jiajia |
Organization: |
University Of South Carolina At Columbia |
Project Title: |
Development and Evaluation of Spatial Survival Models |
Fiscal Year: |
2010 |
Abstract
DESCRIPTION (provided by applicant): Prostate cancer is the most common malignancy in men and a leading cause of cancer mortality among males in the United States. Large geographical variation and racial disparities exist in its survival rate after diagnosis. We will develop new and 0exible statistical methods of spatial survival model to estimate cancer survival. We will apply the proposed method to analyze the prostate cancer data within Louisiana from the Surveillance, Epidemiology, and End Results program and within South Carolina from the South Carolina Central Cancer Registry. We will use this analysis to investigate the spatial patterns and racial disparities of prostate cancer in Louisiana and South Carolina. We will conduct a complete simulation study to compare the performances of existing spatial survival models, and it can help the practitioners or researchers involved in cancer studies to select the right spatial survival model. The proposed method may possibly be extended to include more complex situations in the future, such as groupings and time variations, in epidemiological cancer study.
Publications
A Bayesian normal mixture accelerated failure time spatial model and its application to prostate cancer.
Authors: Wang S.
, Zhang J.
, Lawson A.B.
.
Source: Statistical Methods In Medical Research, 2016 Apr; 25(2), p. 793-806.
EPub date: 2012-11-01 00:00:00.0.
PMID: 23117407
Related Citations
Prior choice in discrete latent modeling of spatially referenced cancer survival.
Authors: Lawson A.B.
, Choi J.
, Zhang J.
.
Source: Statistical Methods In Medical Research, 2014 Apr; 23(2), p. 183-200.
EPub date: 2012-05-02 00:00:00.0.
PMID: 22556109
Related Citations
Bayesian point event modeling in spatial and environmental epidemiology.
Authors: Lawson A.B.
.
Source: Statistical Methods In Medical Research, 2012 Oct; 21(5), p. 509-29.
PMID: 23035034
Related Citations
Bayesian Parametric Accelerated Failure Time Spatial Model and its Application to Prostate Cancer.
Authors: Zhang J.
, Lawson A.B.
.
Source: Journal Of Applied Statistics, 2011 Mar; 38(2), p. 591-603.
PMID: 21475617
Related Citations
Semiparametric Accelerated Failure Time Partial Linear Model and Its Application to Breast Cancer.
Authors: Zou Y.
, Zhang J.
, Qin G.
.
Source: Computational Statistics & Data Analysis, 2011-03-01 00:00:00.0; 55(3), p. 1479-1487.
PMID: 21499529
Related Citations