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Breast cancer survival, competing risks and mixture cure model: a Bayesian analysis

机译:乳腺癌生存率,竞争风险和混合治愈模型:贝叶斯分析

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Cancer is a major public health burden and is the second leading cause of death in the USA. The US National Cancer Institute estimated overall costs of cancer in 2007 at $219.2 billion. Breast cancer has the highest cancer incidence rates among women and is the second leading cause of cancer death among women. The 'Surveillance, epidemiology, and end results' programme of the National Cancer Institute collects and publishes cancer survival data from 17 population-based cancer registries. The CANSURV software of the National Cancer Institute analyses cancer survival data from the programme by using parametric and semiparametric mixture cure models. Another popular approach in cancer survival is the competing risks approach which considers the simultaneous risks from cancer and various other causes. The paper develops a model that unifies the mixture cure and competing risks approaches and that can handle the masked causes of death in a natural way. Markov chain sampling is used for Bayesian analysis of this model, and modelling and computational issues of general and restricted structures are discussed. The various model structures are compared by using Bayes factors. This Bayesian model is used to analyse survival data for the approximately 620000 breast cancer cases from the programme. The estimated cumulative probabilities of death from breast cancer from the proposed mixture cure competing risks model is found to be lower than the estimates that are obtained from the CANSURV software. Whereas the estimate of the cure fraction is found to be dependent on the modelling assumptions, the survival and cumulative probability estimates are not sensitive to these assumptions. Breast cancer survival in different ethnic subgroups, in different age subgroups and in patients with localized, regional and distant stages of the disease are compared. The risk of mortality from breast cancer is found to be the dominant cause of death in the beginning part of the follow-up whereas the risk from other competing causes often became the dominant cause in the latter part. This interrelation between breast cancer and other competing risks varies among the different ethnic groups, the different stages and the different age groups.
机译:癌症是主要的公共卫生负担,并且是美国第二大死亡原因。美国国家癌症研究所估计,2007年的癌症总成本为2192亿美元。乳腺癌是女性中癌症发生率最高的疾病,是女性癌症死亡的第二大主要原因。国家癌症研究所的“监视,流行病学和最终结果”计划收集并发布了来自17个基于人群的癌症登记处的癌症生存数据。美国国家癌症研究所的CANSURV软件通过使用参数和半参数混合物治愈模型来分析该程序的癌症存活数据。癌症生存中的另一种流行的方法是竞争风险方法,它考虑了癌症和各种其他原因引起的同时风险。本文开发了一个模型,该模型统一了混合疗法和竞争风险方法,并且可以自然方式处理隐蔽的死亡原因。使用马尔可夫链采样对该模型进行贝叶斯分析,并讨论了一般结构和受限结构的建模和计算问题。使用贝叶斯因子比较各种模型结构。该贝叶斯模型用于分析该计划中大约62万例乳腺癌病例的生存数据。发现从提议的混合治疗竞争风险模型中估计的乳腺癌死亡累积概率低于从CANSURV软件获得的估计值。尽管发现治愈分数的估计值取决于建模假设,但生存期和累积概率估计值对这些假设并不敏感。比较了不同种族亚组,不同年龄亚组以及局部,区域和远期疾病患者的乳腺癌生存率。在随访的开始部分,发现乳腺癌的死亡风险是主要的死亡原因,而在其他方面,其他竞争原因的风险通常成为主要的死亡原因。乳腺癌与其他竞争风险之间的这种相互关系在不同种族,不同阶段和不同年龄组之间有所不同。

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