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Bayesian area-age-period-cohort model with carcinogenesis age effects in estimating cancer mortality

机译:具有致癌年龄效应的贝叶斯地区-年龄-时期-队列模型估计癌症死亡率

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Objective: Area-age-period-cohort (AAPC) model has been widely used in studying the spatial and temporal pattern of disease incidence and mortality rates. However, lack of biological plausibility and ease of interpretability on temporal components especially for age effects are generally the weakness of AAPC models. We develop a Bayesian AAPC model where carcinogenesis age effect is incorporated to explain age effects from the underlying disease process. An autoregressive prior structure and an arbitrary linear constraint are used to solve the nonidentifiability issues. Methods: Two multistage carcinogenesis models are employed to derive the hazard functions to substitute the age effects in the AAPC models. The Iowa county-wide lung cancer mortality data are used for the model fitting and Deviance Information Criteria (DIC) is used for model comparison. Results: Our study shows that conventional AAPC model (DIC = 19,231.30), AAPC model with Armitage-Doll age effect (DIC = 19,233.00) and with two-stage clonal expansion (TSCE) age effect (DIC = 19,234.70) achieved the similar DIC values which indicated consistent model fitting among three models. The spatial pattern shows that the high spatial effects are clustered in the south of Iowa and also in largely populated areas. The lung cancer mortality rate is continuously declining by birth cohorts while increasing by the calendar period until 2000-2004. The age effects show an increasing pattern over time which can be easily explained by Armitage-Doll carcinogenesis model since we assume a log-linear relationship between age and hazard function. Conclusions: Our finding suggests that the proposed Bayesian AAPC model can be used to replace the conventional AAPC model without affecting model performance while providing a more biological sound approach from the underlining disease process.
机译:目的:区域年龄段队列(AAPC)模型已被广泛用于研究疾病发病率和死亡率的时空格局。但是,缺乏生物学上的合理性和对时间成分的解释容易,特别是对于年龄的影响,通常是AAPC模型的弱点。我们建立了贝叶斯AAPC模型,其中纳入了致癌年龄效应,以解释潜在疾病过程的年龄效应。使用自回归先验结构和任意线性约束来解决不可识别性问题。方法:采用两种多阶段致癌模型推导危害函数,以替代AAPC模型中的年龄效应。爱荷华州全县肺癌死亡率数据用于模型拟合,而偏差信息标准(DIC)用于模型比较。结果:我们的研究表明,传统的APC模型(DIC = 19,231.30),具有Armitage-Doll年龄效应(DIC = 19,233.00)和两阶段克隆扩展(TSCE)年龄效应(DIC = 19,234.70)的AAPC模型获得了相似的DIC值这表明三个模型之间的模型拟合一致。空间格局表明,高空间效应聚集在爱荷华州南部以及人口稠密地区。肺癌死亡率因出生人群而持续下降,而直到2000-2004年日历期才有所增加。年龄效应显示出随时间增加的模式,这可以用Armitage-Doll致癌模型轻松解释,因为我们假设年龄与危险功能之间呈对数线性关系。结论:我们的发现表明,所提出的贝叶斯AAPC模型可以用来代替传统的AAPC模型,而不会影响模型的性能,同时从强调疾病的过程中提供了更生物学的方法。

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