Abstract

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.

Public Health Relevance

This project will develop new and ?exible statistical methods of spatial survival model to estimate cancer survival and conduct a complete simulation study to compare the performances of existing spatial survival models. We will use this analysis to investigate the spatial patterns and racial disparities of prostate cancer in Louisiana and South Carolina.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Small Research Grants (R03)
Project #
1R03CA139538-01
Application #
7662933
Study Section
Special Emphasis Panel (ZCA1-SRRB-D (J1))
Program Officer
Mariotto, Angela B
Project Start
2009-09-01
Project End
2011-08-31
Budget Start
2009-09-01
Budget End
2010-08-31
Support Year
1
Fiscal Year
2009
Total Cost
$76,540
Indirect Cost

  Institution

Name
University of South Carolina at Columbia
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
041387846
City
Columbia
State
SC
Country
United States
Zip Code
29208

  Publications

Wang, Songfeng; Zhang, Jiajia; Lawson, Andrew B (2016) A Bayesian normal mixture accelerated failure time spatial model and its application to prostate cancer. Stat Methods Med Res 25:793-806
Lawson, Andrew B; Choi, Jungsoon; Zhang, Jiajia (2014) Prior choice in discrete latent modeling of spatially referenced cancer survival. Stat Methods Med Res 23:183-200
Lawson, Andrew B (2012) Bayesian point event modeling in spatial and environmental epidemiology. Stat Methods Med Res 21:509-29
Zou, Yubo; Zhang, Jiajia; Qin, Guoyou (2011) Semiparametric Accelerated Failure Time Partial Linear Model and Its Application to Breast Cancer. Comput Stat Data Anal 55:1479-1487
Zhang, Jiajia; Lawson, Andrew B (2011) Bayesian Parametric Accelerated Failure Time Spatial Model and its Application to Prostate Cancer. J Appl Stat 38:591-603