Literature DB >> 18416442

Joint spatial survival modeling for the age at diagnosis and the vital outcome of prostate cancer.

Huafeng Zhou1, Andrew B Lawson, James R Hebert, Elizabeth H Slate, Elizabeth G Hill.   

Abstract

Prostate cancer (PrCA) 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 both the incidence of PrCA and the survival rate after diagnosis. In this population-based study, a joint spatial survival model is constructed to investigate factors that affect the age at diagnosis of PrCA and the subsequent survival. The joint model for these two time-to-event outcomes is specified through parametric models for age at diagnosis and survival time conditional on diagnosis age. To account for possible correlation in these outcomes among men from the same geographical region, frailty terms are included in the survival model. Both spatially correlated and uncorrelated frailties are incorporated in each model considered. The deviance information criterion is used to select a best-fitting model within the Bayesian framework. The results from our final best-fitting model indicate that race, marital status at diagnosis, and cancer stage are significantly associated with both of the two time-to-event outcomes. No pattern emerged in the geographical distribution of age at PrCA diagnosis. In contrast, a spatially clustered pattern was observed in the geographic distribution of survival experience post diagnosis. 2008 John Wiley & Sons, Ltd

Entities:  

Mesh:

Year:  2008        PMID: 18416442      PMCID: PMC3417137          DOI: 10.1002/sim.3232

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  17 in total

Review 1.  Patterns and trends in prostate cancer incidence, survival, prevalence and mortality. Part II: individual countries.

Authors:  M Quinn; P Babb
Journal:  BJU Int       Date:  2002-07       Impact factor: 5.588

2.  Rural-urban differences in usual source of care and ambulatory service use: analyses of national data using Urban Influence Codes.

Authors:  Sharon L Larson; John A Fleishman
Journal:  Med Care       Date:  2003-07       Impact factor: 2.983

3.  Frailty modeling for spatially correlated survival data, with application to infant mortality in Minnesota.

Authors:  Sudipto Banerjee; Melanie M Wall; Bradley P Carlin
Journal:  Biostatistics       Date:  2003-01       Impact factor: 5.899

4.  Person and place: the compounding effects of race/ethnicity and rurality on health.

Authors:  Janice C Probst; Charity G Moore; Saundra H Glover; Michael E Samuels
Journal:  Am J Public Health       Date:  2004-10       Impact factor: 9.308

5.  Prostate cancer in Italy before and during the 'PSA era': survival trend and prognostic determinants.

Authors:  A Quaglia; M Vercelli; A Puppo; C Casella; E Artioli; E Crocetti; F Falcini; V Ramazzotti; G Tagliabue
Journal:  Eur J Cancer Prev       Date:  2003-04       Impact factor: 2.497

6.  Trends in prostate cancer mortality among black men and white men in the United States.

Authors:  Kenneth C Chu; Robert E Tarone; Harold P Freeman
Journal:  Cancer       Date:  2003-03-15       Impact factor: 6.860

7.  International trends and patterns of prostate cancer incidence and mortality.

Authors:  A W Hsing; L Tsao; S S Devesa
Journal:  Int J Cancer       Date:  2000-01-01       Impact factor: 7.396

Review 8.  Prostate cancer disparities in South Carolina: early detection, special programs, and descriptive epidemiology.

Authors:  Bettina F Drake; Thomas E Keane; Catishia M Mosley; Swann Arp Adams; Keith T Elder; Mary V Modayil; John R Ureda; James R Hebert
Journal:  J S C Med Assoc       Date:  2006-08

Review 9.  Update on screening for prostate cancer with prostate-specific antigen.

Authors:  Hans-Peter Schmid; Walter Riesen; Ladislav Prikler
Journal:  Crit Rev Oncol Hematol       Date:  2004-04       Impact factor: 6.312

Review 10.  Patterns and trends in prostate cancer incidence, survival, prevalence and mortality. Part I: international comparisons.

Authors:  M Quinn; P Babb
Journal:  BJU Int       Date:  2002-07       Impact factor: 5.588

View more
  3 in total

1.  A spatial beta-binomial model for clustered count data on dental caries.

Authors:  Dipankar Bandyopadhyay; Brian J Reich; Elizabeth H Slate
Journal:  Stat Methods Med Res       Date:  2010-05-28       Impact factor: 3.021

2.  Bayesian modeling of multivariate spatial binary data with applications to dental caries.

Authors:  Dipankar Bandyopadhyay; Brian J Reich; Elizabeth H Slate
Journal:  Stat Med       Date:  2009-12-10       Impact factor: 2.373

3.  Evaluation of the performance of tests for spatial randomness on prostate cancer data.

Authors:  Virginia L Hinrichsen; Ann C Klassen; Changhong Song; Martin Kulldorff
Journal:  Int J Health Geogr       Date:  2009-07-03       Impact factor: 3.918

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.