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Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer registry data.
Wang, Ming; Li, Zheng; Lu, Jun; Zhang, Lijun; Li, Yimei; Zhang, Liangliang.
Afiliación
  • Wang M; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA. mxw827@case.edu.
  • Li Z; Novartis Pharmaceuticals, East Hanover, NJ, USA.
  • Lu J; Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois Chicago, Chicago, IL, USA.
  • Zhang L; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
  • Li Y; Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
  • Zhang L; Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA.
BMC Med Res Methodol ; 24(1): 86, 2024 Apr 08.
Article en En | MEDLINE | ID: mdl-38589783
ABSTRACT
Prostate cancer is the most common cancer after non-melanoma skin cancer and the second leading cause of cancer deaths in US men. Its incidence and mortality rates vary substantially across geographical regions and over time, with large disparities by race, geographic regions (i.e., Appalachia), among others. The widely used Cox proportional hazards model is usually not applicable in such scenarios owing to the violation of the proportional hazards assumption. In this paper, we fit Bayesian accelerated failure time models for the analysis of prostate cancer survival and take dependent spatial structures and temporal information into account by incorporating random effects with multivariate conditional autoregressive priors. In particular, we relax the proportional hazards assumption, consider flexible frailty structures in space and time, and also explore strategies for handling the temporal variable. The parameter estimation and inference are based on a Monte Carlo Markov chain technique under a Bayesian framework. The deviance information criterion is used to check goodness of fit and to select the best candidate model. Extensive simulations are performed to examine and compare the performances of models in different contexts. Finally, we illustrate our approach by using the 2004-2014 Pennsylvania Prostate Cancer Registry data to explore spatial-temporal heterogeneity in overall survival and identify significant risk factors.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Modelos Estadísticos Límite: Humans / Male Idioma: En Revista: BMC Med Res Methodol Asunto de la revista: MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Próstata / Modelos Estadísticos Límite: Humans / Male Idioma: En Revista: BMC Med Res Methodol Asunto de la revista: MEDICINA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido