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Spatial-temporal Bayesian accelerated failure time models for survival endpoints with applications to prostate cancer registry data.

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第一作者： Ming,Wang

第一单位： Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA. mxw827@case.edu.

作者： Ming,Wang ^[1] ; Zheng,Li ^[2] ; Jun,Lu ^[3] ; Lijun,Zhang ^[4] ; Yimei,Li ^[5] ; Liangliang,Zhang ^[4]

作者单位： Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA. mxw827@case.edu. ^[1] Novartis Pharmaceuticals, East Hanover, NJ, USA. ^[2] Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois Chicago, Chicago, IL, USA. ^[3] Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA. ^[4] Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA. ^[5]

关键词 Accelerated failure times Bayesian inference Monte Carlo Markov chain Multivariate conditional autoregressive priors Prostate cancer Spatial-temporal modeling

医学主题词男(雄)性(Male);人类(Humans);模型, 统计学(Models, Statistical);贝叶斯定理(Bayes Theorem);比例危险度模型(Proportional Hazards Models);前列腺肿瘤(Prostatic Neoplasms);Markov链(Markov Chains)

DOI 10.1186/s12874-024-02201-w

PMID 38589783

发布时间 2024-04-11

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